How Agents Decide: Goodfire's Eric Bigelow on Critical Tokens, Phase Shifts, & In-Context Learning

Welcome and Episode Overview

0:00Hello, and welcome back to The Cognitive Revolution. Today my guest is Eric Bigelow, member of technical staff at Unicorn Mechanistic Interpretability Startup, Goodfire. The story behind this episode is unique. After recently speaking with Bronson Shane of Apollo Research, who explained that even with access to models' internal chain of thought, it is still often extremely difficult to determine how a model will decide to act, I asked Claude to survey the literature to see what the field as a whole understands about how these critical decision

0:32tokens are chosen. One name kept coming up in that thread, and it was Eric. Eric recently completed a PhD in the Harvard's psychology department, with a dissertation titled Toward a Cognitive Science of Large Language Models. And while some of his academic colleagues initially questioned his pivot to focus on LLMs, the fact that even elite academic institutions are now willing to engage with AIs as a sort of mind, strikes me as very notable indeed. We start today with a survey of

1:05Eric's work over the last couple of years, beginning with his 2024 paper on Forking Paths in Neural Text Generation, in which he conducted a massive resampling experiment at every token of a model's chain of thought, to identify the critical tokens where uncertainty around the final answer suddenly collapses. As so often with LLMs, some of the findings were intuitive, while others were quite surprising, and at times difficult to interpret. Interestingly, Eric says that a system's decisions are actually taken not so much by the model itself, but by the process of sampling from the

1:40model's final token distribution. Which means that while the stochastic parrot paradigm should definitely be retired, stochasticity still plays an important role in model behavior.

1:52Eric frames much of what we are seeking to understand about LLMs in terms of in-context learning, emphasizing that few-shot prompts are just one clear example of how much runtime context can shape model behavior. His bet is that this flexibility means that improved harnesses and memory systems will out-compete per-user fine-tuning, and thus that our focus of study should remain on major foundation models. Along the way, we also discuss why 7 to 8 billion parameters currently seems to be the interpretability sweet spot, why KimiK3 was a step function for studying frontier-level coding

2:28and reward hacking, and why, for now at least, interpretability outside of the frontier model companies does meaningfully depend on Chinese open-weights models. We also get Eric's thoughts on why chain of thought is getting so weird, including his perspective on the chain of thought dialect and the strange language that Astro uses to communicate with sub-agents. Plus his pro-tips for using Goodfire's Silico research agent, and how he hopes future AI product interfaces will begin to resurface model uncertainty to users.

3:00The bottom line from this conversation and others I've had recently on the future of AI monitoring and control is that chain of thought is increasingly unreliable, and that because these issues seem to correlate not just with RL training, but with the power of the raw pre-trained model itself, this trend might be quite difficult to reverse. That in turn means that an awful lot, including our ability to understand why agents take the actions they do, may rest on interpretability.

3:33Eric is a pioneer of this research, but as you'll hear, tons of work remains to be done. With that, I hope you enjoy this overview of what we know and how much we have left to discover about how AIs decide what to do. With Eric Bigelow, from Goodfire.

Forking Paths and Neural Generation

3:53Eric Bigelow, member of technical staff at Goodfire, welcome to the Cognitive Revolution. Thank you very much, Nathan. Excited to be here. Yeah, I'm excited for this conversation too. I think this might be the first time in the history, and there's been like 400 episodes of the podcast at this point, that I went to AIs looking for an answer for a very specific question and came back with a name, and that name was yours. So the kind of upstream conversation I had of this one was with Bronson Shane at Apollo, who has the dubious

4:26distinction perhaps of having read maybe more chain of thought reasoning than like any other living human, at least any living human who can talk about it all publicly. And one of the big takeaways I had from that conversation was he said, you look at these chains of thought, and the models are thrashing around doing a sort of linearized tree search of possibility. And then at some point, they make a decision. And even having read all the chain of thought that came before that,

4:58it's often like very not obvious why they made the decision that they made. And you really couldn't have predicted it. And in a way, that's like kind of human-like, right? How do I make my decision? I'm not so sure I have great insight into that all the time either. But this prompted the question, okay, at some point after all this stuff, all this reasoning, all this, again, trying to reason about what the greater wants and all that stuff, eventually we come to a passage and it's like, and so the answer is, how does that next token get chosen? That's what I went to the AIs to ask. Asked for a literature review. And Eric Bigelow came back as the through

5:32line through that research. And I was like, I know that company. I should get to know that guy. So what I want to do, I think you may know as much or more than any other living human about how these decisions are made. And I'm afraid that's maybe not still all that much, but what I hope to get out of this conversation is a little intellectual history of your work and like the up-to-date understanding of how the hell do these things make decisions? What do we know about that? And what is our plan to figure out how to understand it better? Because obviously they're making more

6:04and more consequential decisions all the time. How does that sound? I'm very honored to be the first guest chosen by the AIs. I always, I feel like they, when I talk to Claude, it seems to really get excited about my research, but I'm sure everybody feels that way. I think, uh, I think actually talking, having some conversations around, uh, around this podcast and some of the things you were asking about makes me think that I actually should devote more effort to this exact question

6:34of how decisions are made. But I'll give a quick review of, um, a project that I think is really one of the most informative projects and how I think about how LLMs work at a basic level. And that's, uh, my project forking paths and neural text generation. And for that, basically, so this was two years ago with, at the time it was one of the GPT 3.5 models. And what we do is we take, uh, reasoning chains. We give it a chain of, we give it a question and a chain of thought prompt and have it reason through,

7:07you know, step-by-step. And then every single token in its reasoning, we resample all these completions. So we resample like 30 different rollouts at every single token. And then from every one of those rollouts, we see what final answer it landed on. And you can aggregate these into various distributions and then do analysis on these distributions. But I think the most striking part of this is if you arrange it like a multivariate time series, you see these really interesting dynamics that are happening. And, um, I kind of take inspiration from this, from some of the

7:41learning dynamics work that was coming out around that time, like around grokking and things like this, where there are like these really sharp phase transitions and in training, and the model just seems to suddenly learn something. And it turns out that you actually have these during reasoning and in context learning as well, where over the course of reasoning, there will be certain points in reasoning that the model was initially sort of at a 50% chance of this answer and 30% chance of this answer. And then there's some point that it hits where suddenly the distribution changes really

8:14dramatically. And often the distribution just kind of collapses onto a final answer as the model sort of, you might say, decides what it's going to say next. Although I think, um, the, I use the word decides here with maybe scare quotes around it. Cause I think there's some nuance to what that means, but one of my big takeaways from this, and, um, I think this also just kind of falls out of thinking about the architecture, but it's nice to have results that really support this too, is that models, even to some extent, when they plan out their responses, what they're actually going to say

8:47at every single moment is really determined by like the flip of a coin, which is what token will it generate next? And, um, there is sort of an interface that was really standard and with opening eye, at least at the time, which I think is unfortunately no longer present. And I haven't really seen standard interfaces, which is where you could see the log probabilities of all the tokens in a text output you got. And you could kind of hover over different tokens and see what are the alternate tokens that could have been generated instead. And if you do this with a lot of text generation,

9:19you might realize that you get this one text answer when you ask some question, but really there actually could have been a lot of different answers. And you might ask what would have happened if this word was different from this word. And, um, what we found is that there are certain key words where sometimes the words, if you were to sort of like look at it, like a judge would, you might guess this like the first time that a final answer is mentioned in the reasoning chain. But then there's other times where this distribution changes really dramatically on some sort of arbitrary word, even like a open parenthesis for like, you know, there's one example we have in the paper where it's like

9:53kilowatt hours, open parentheses, KWH. And, you know, you never expect that to really change how the final answer plays out. But if you actually go to that exact token with this model and you just resample a bunch of text generations, you see that in cases where it's not an open parenthesis, where it starts to be some other just arbitrary word, it ends up being a different final answer. And so, um, I think this, this thinking about decisions in LLMs or what they're going to say next very much being

10:25determined by sort of chance and by whatever tokens are sampled is really, I think the foundation for how I think about a lot of things in LLMs. And I think it sort of shapes how I think about like beliefs and models and sort of what's considered like a factor of hallucination can be dependent on what tokens were generated before it. And, uh, and I think one of the properties of reasoning that really comes into play here is self-consistency. So if there's a long reasoning chain that is self-consistent and it sort of, and it sort of generates some token earlier in the chain of

10:59suppose that this was the year instead of this was the year, if, if it thinks that the year is 2024 and 20, instead of 2026, actually the answer to a different, to a question might be very different. And there's cases like this where it's like you, you might think, well, obviously there's like a ground truth. If I'm asking who is, uh, the current head of state, there is a current year, which right now is 2026. And so the model was hallucinating, but I think there's also a lot of use cases with LLMs where we don't really know what the ground truth are. And, and there's sort of

11:30this, this branching space of possibilities that we actually want to explore naturally when we're interacting with a model. And, um, like, for example, if I'm working on a scientific research project and I want to sort of talk to my LLM about like, what should I do next? Or what, what did my experiment results mean? I might really want to kind of like iterate and understand what's this full like space, this whole tree of possibilities that it could have generated and what would have been different if it had assumed a instead of B. And so to, to go back

12:01to your question, I think the, the simple answer I have is that I think this decision really happens during sampling is my biggest part of how I think about this is that again, every, every token that's generated by the model to I, one of the other big influences in my work is thinking about in context learning and how models adapt to context. And the way I think about reasoning is that models are basically in context learning from things that they have generated already. So the words that they generate, there's sort of this stochasticity that's happening. There's

12:37these coin flips that are happening as it's, you could say deciding, but it's really just a sampling from a distribution of these alternate tokens, but depending on what token is sampled it in context learns. And there's this context dependence where then it'll generate a set of reasoning that is consistent with that. And, um, I think, I think that like, I'm pretty bullish around like Macinterp shedding more light, I think on really answering this question of how decisions are made.

13:08But then, um, I think, I think, so I said that this work was done a couple of years ago and I think that modern reasoning models are a little bit, are a little bit different than chain of thought reasoning was then. And, um, some recent projects I've worked on, even with like Macinterp of reasoning models have kind of, uh, changed a little bit how I think about what these models are doing. And so I think, I think there's still context dependence, but actually, as you, as you mentioned, reasoning models, I think often tend to do this like really like linearized search where they like enumerate all

13:42these different possibilities in, in context. And then at the end, they sort of just go back and pick one of the things that they said before. So I think this, this might change exactly how this happens. From what I've seen, the uncertain, the uncertainty dynamics, if you do this, like forking path analysis on reasoning models is often a bit smoother, but there are still sometimes these forking points where if one token is generated different from another, or if the model just really doesn't, you know, it enumerates all these reasoning chains and none of them really seem that much more promising than another, then eventually it does sort of, there are cases where

14:17it will just say, okay, the answer therefore is B, C, and it actually could have been different depending on what was generated. But I think there could still be more work done in, in, in understanding the internal processes that, that lead to that, that point of choice even. And, and another project that I'm on has, has shown that there are at least with some, some other toy domains, there are these cases of like neurons that seem to like track model confidence where you can even like steer and make the

14:49model more or less confident generically about whatever concept it's learning. But yeah, I think, I think we really need more interpretability work in this vein. And I think in context, learning and reasoning are unfortunately vastly neglected in the world of, of interpretability compared to how I think how defining they are for how we think about lots of the high level things that LLMs do.

15:16Yeah. I mean, the, when you talk to the guy who's read more chain of thought than anyone, and you keep in mind that the, a huge amount of the frontier company's plans right now rests on chain of thought monitoring as like the way we're going to keep track of what the AIs are doing and if they're doing the right or wrong thing. And then you hear that. Yeah. Even having read it all and being like fluent in these like strange internal dialects that they use, I still can't tell you why it chose what it chose. It's all yikes. Where's the red phone to call the interpretability department?

Sponsors and Commercial Breaks

15:47Hey, we'll continue our interview in a moment after a word from our sponsors.

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In-Context Learning and Adaptation

18:46I think there's a few different directions you started to touch on there that I would like to follow up on. Maybe for starters, let's go on this relationship between in-context learning and other things, right? Like there's been a lot of work that's looked at, if not, this is maybe too strong, but sort of an isomorphic relationship between in-context learning and fine-tuning. I think

19:17that's like especially true at LoRa scale fine-tuning where you can localize the weight changes. Maybe it's also true even if you're doing like full weight fine-tuning. I'm less sure about that. How should we understand that relationship? What do you think is most important that may not be obvious? So I think, I think the most important thing I'd say about in-context learning that may not be obvious for people, especially if you're, if you're just getting started learning

19:49about it, is that I think when people say in-context learning historically, it's meant this sort of traditional few shot, give a few shots of input-output examples and see how the model behavior changes. And you can do these like scaling curves, for example, where you like increase the number of shots that you give and see how model important performance improves as a function of this. But then I think, um, the way I think of in-context learning, and I think there's a few, there's a few other people that I've, have echoed this. And I, one, one paper that I like around this

20:22is by, uh, Andrew Lamben and others on the, I think it's called the broader spectrum of in-context learning, where I think, what I think of in-context learning as capturing is everything the model is doing to adapt its behavior. That's not happening within weights learning. And so this is how it's changing its representations as it reads each new word of input text. And this also applies to reasoning where it, with reasoning, it's generating text and then using that to determine

20:53its following behavior. And, uh, and so I think, I think this view of in-context learning is sort of being really like ubiquitous. I think it, it gives some teeth to thinking about prompting where like there's this term prompt engineering that is sort of a little bit of like a dirty word, I think, in the scientific community where it feels like people just come up with like a bunch of hacky solutions that make the models go better, you know, tell it you're a brilliant software engineer and it works and it writes code better, these sorts of things. Although these are maybe a bit less prevalent today because maybe we've kind of, I don't know, fine-tuned some of that,

21:27some of that distribution shift just into the models. But I think, um, I think there's a way that it really defies comparison to like this kind of in-weights learning where really prompting is, that's not going to disappear no matter how much fine-tuning you give. You're going to have different contexts that you give the model and you want it to adapt its behavior dynamically, right? Like I think even if you, I think fine-tune a model enormously, like you still want it when it's like reading, when it's reading Harry Potter for the first time, I mean, of course it's in the

21:57training data, but suppose there is another, you know, story it was reading for the first time. You'd want it to be updating its beliefs internally about what's going on in the story. Who are the good guys and the bad guys? Who's, who is happy and who is sad at different times? And really like, um, I, I have another paper around this sort of, uh, belief updating and representation change and stories. And I, and I kind of liked this metaphor because I think it, it's easy to understand that, you know, as you read a story, your beliefs should change. There's, there's a bunch of work around

22:28analyzing the sentiments of stories. And one of the inspirations from that project was this is famous talk by Kurt Vonnegut on the shape of stories where he was describing like these different stories, like Cinderella starts the Cinderella is very sad. Everything's horrible. And then she gets invited to the ball and everything's great for a little while. And then the, and then the, the magic sort of falls apart and she has to go back to her normal life, but a bit better than she was before. And then finally at the end, when, when the Prince finds her, you know, she has

22:59a infinitely happy ending or, or however he says that. And I think like stories have shapes and really all contexts that we give models also have shapes that are dynamic and temporal. And I think this is something you just, it's, it's hard to really compare it to fine tuning or to these other ways of changing representations. And I think more broadly, I think that this is a piece of the puzzle, the thinking about interpretability of LLMs and not just feed forward networks is that

23:29everything that LLMs are doing is happening dynamically. They're updating their beliefs, they're shifting representations internally, they can learn new representations in context. And, um, this doesn't just happen when you give a few input output examples, this is happening all the time. This is happening over a conversation. This is happening within sentences. This is happening during the period where a model is reasoning or generating a really long input or output text. It's sort of changing its representations dynamically to adapt what it's doing in the

24:02moment. And, uh, yeah, I think, um, I think a lot of work kind of misses these dynamic changes that are happening. I think a lot of, a lot of interpretability work sort of focuses on these like templates where there's just sort of a couple, a couple of variables that are changed in the input and behavior is mentioned on like one token or one, or one, or one, one sort of output variable. And really what LLMs are doing is I think much more dynamic and adaptive all the time. One other point I want to, I want to sort of

24:38raise on this view of in context learning is that, uh, something I was inspired by too at the time with, with grokking in this kind of work was, um, this idea that, that if you look at learning curves over a huge data set, they'll be very, very smooth. And as you just keep training the model and scaling parameters, things will just very, very smoothly increase as you, as you train it for longer and longer or errors will decrease rather. But then if you look at specific problems, specific problems can actually change very sharply. There actually can be these phase transitions. And, uh, and I think it

25:14was, it was Neil Nanda who had this quote of like phase transitions are everywhere. If you just looked closely enough, if you broke your data set down into like all the, into just individual tasks and you looked at individual tasks, there'd be a lot of stepwise changes where the model really seemed to just suddenly learn something. And I think in context learning is like that too, where if you look at aggregate, if you look across a large data set, things might look smooth, maybe deceptively. So, but actually individual cases like an individual story have these really non-linear dynamics and, uh,

25:48curves can change very sharply. Things can change after even just one, one token or one sentence and, uh, other properties can change, not just, you know, suddenly learning something, but beliefs can change. Beliefs can go up and down and follow all sorts of, all sorts of weird twists and turns. And so I think, um, another, another thing that I think is sometimes missing from views of in-context learning is this, you know, zooming onto one example sort of problem. And, um, something that

26:18I think is also like, um, I'm, I'm really curious if the, if the person you had earlier, who's looked at all these chains of thought, if he's looked at, uh, like resampled outputs for the same model or kind of done some of this, like resampling within a single chain of thought, because I think sometimes you can get a lot from looking at patterns by just like looking at like a ton of different like rollouts and just like trying to understand like how the model thinks in some

26:49general way. But then I think like for an individual prompt, there's sort of this like really elaborate landscape just in how the model will think about one problem. And I think sometimes you can only get this by looking at a lot of different resampled outputs for that same problem and seeing like where are the patterns, what kinds of things are similar or different. Like, uh, if you, if you give models, depending on which model you, you have, if you give them a very open-ended prompt, like generate a story, sometimes you'll notice these really funny patterns where they'll

27:22write very different story. Like things will be superficially very different where it'll kind of start out like very different, but then they kind of like, I, I started out that Forking Paths project by just telling me a story about a boy named John and like half the stories involve John having a dog and they go on an adventure and a whole lot of those involve them eventually finding pond or a waterfall or something. And then like sometimes, uh, the dog saves the day or sometimes John saves the day. But I think these are all these different patterns you see for outputs for these

27:55same input example. And so I think, I think we need to do more of that, like looking at like a lot of resampled outputs for the same input examples to kind of understand like how bumpy or smooth the landscape of in-context learning is for these models. Hey, we'll continue our interview in a moment after a word from our sponsors. The Cognitive Revolution is brought to you and powered by Tasklet. I've been running Tasklet agents for almost a year, and they've become a major part of how the show gets made. Before every recording, one agent researches the guest and sends me a suggested

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29:33teammates. Start with one recurring job, like researching the people you're meeting. And then just keep building from there. Visit tasklet.ai and use code COGREV for $50 in free credits. That's code COGREV at tasklet.ai. One big thing I'm pushing toward is continual learning. And I wonder what your

Continual Learning and Interpretability

30:03experience trying to understand in context learning suggests or implies about how much additional challenge full continual learning might present. I could imagine, like, I think the naive read would be like, this is going to be really hard now because we've got everybody's individual model becomes personalized over time and who's to say how different they are. But then the, maybe the counter argument would be like, we already have that within context learning because there's a lot of context

30:36and we basically already deal with that same problem. Does your experience make you feel like we're ready for continual learning in terms of our interpretability techniques or that it will still pose like a step change, increase in difficulty? I think, um, I think for your question of whether our current interpretability techniques will face challenges with, with continual learning. I think it really depends on how continual learning plays out. And, um, it's something that I haven't like personally done research on or thought too much

31:10about in depth on like, do we need different architectures for this? And I know there's sort of a lot of people that have been debating this for a while. And as you're saying, like having personalized models or having models that are like fine tuned your data. I think if that's the case, then there's a possibility that we'll have to have interpretability or interpretability techniques will have to evolve as well. And we'll have to have some sort of like, like, I think, I think how, how well, you know, say, say that you have a tool that's trained on a base model and then that model is fine tuned for 10 different people or 10 million different people. I think how the real key question

31:47there and how well your tools will directly translate is how much representation change there is. And like, um, I have some work on this and, and other folks at good fire have some really cool work on there being these low dimensional manifolds that describe the sort of underlying representation space that models are going through as you're, as you're interacting with them. And as, and a lot of the representations are sort of defined by the slow, low dimensional conceptual structure. And I think

32:17my intuition would be that as long as a lot of that structure remains the same, then a lot of the techniques will be pretty easy to translate. And maybe you need to have a bit of fine tuning of your probes so that they work on each person's particular fine tuned model in that world. But I think the only real challenge there is if the continual learning leads to such catastrophic forgetting that the internal representation structure of the model radically changes, but I kind of, I kind of doubt that'll happen because that I think that will probably mean that the models will break. Like

32:51it may be that they, there is massive internal representational reorganization, but things are still structured and the model can still do lots of like all the old things it can do, but in new ways. But it seems to me more likely that if, if we're in a world where really there is this collapse of common internal structure, then the models are probably, probably breaking in that case, unless in this world of continual learning, we're using an ungodly amount of compute for every one of these 10 million people to, to train them on, you know, do, do a lot of meta learning so that they maintain their

33:23performance on all these tasks and learn entirely new representational structures. So yeah, I think, I think in that world, it's kind of like having like a bunch of slight tweaks on a main model and we probably will have like tools that we can directly translate to these fine tuned models and maybe just do like a little bit of fine tuning on, on, on the tools that we have and the probes and such to the question about thinking about continual learning and in context learning, my personal view, I mean, again, I don't have strong perspectives around their, around the architecture argument, but I think there's

33:58sort of a, a bit of a, a bitter lesson around like prompting and in context learning and context engineering and harnesses and all that, that they might be all you need for quite a lot of stuff. And I have a bias towards this cause this is what I work on, but I kind of, for me, the most likely outcome in the, in any short term is that we don't have custom models that are really trained on very different data, but they're actually, what's happening is it's different context, right? As you were saying, this is sort of what already happens. We have different LLMs that have different

34:30memories and the memory is just the way I see it. A bunch of data that is tokens that are in context learned every time the model generates an answer, it, it sees this, it sees this long memory. And, um, I think that it's just, it's incredibly easy to get a lot of, a lot of juice out of that without squeezing too hard. And then you end up with these memories that are pretty interpretable. And, uh, yeah, I think, I think some variables would have to change in the equation for me to see it being really that promising to fine tune models separately on individual people's data to

35:04have customization really pay off in that way. And so maybe my, my, my personal best guess is that we're going to see more context engineering and more of this, this kind of thing, more harnesses, more memories. And I think that's really what we've seen in the last year or so with, with the rise of agents is, is, is a lot of, a lot of that engineering is just going into like putting a lot of stuff in context. Yeah. It's been striking that Anthropic has never had a fine tuning product, at least that they've offered to the retail developer customer and open AI has killed theirs.

35:41And what little test cases we do have around interpretability applied to different architectures have been for me, like quite pleasant surprises in terms of how well techniques have translated. I'm thinking for example of Othello GPT and then like Othello Mamba, both were basically interpretable and seem to have like similar representations internally. And those were recovered through like fairly similar techniques. And yeah, long way, those favorable trends continue on the question of

36:13kind of change in models and like how much the, the beasts themselves have morphed in front of our eyes over the last couple of years. I'm old enough to remember when temperature was a feature that we got to play with at the API level. And at that time I knew how my tokens were being sampled from the distribution subject to like, we're going to see for, there was a time I'm also old enough to

36:44remember a time when I didn't know about GPU in determinacy. And I was like, why would I turn my temperature to zero? Do I not get the same thing every time? And that turned out to be the answer for that. But I guess how, how would you describe, because it sure, the temperature has gone away. It sure seems like the models are, you know, mode class is maybe a little bit strong, but like they're quite overfit in some ways. Your story example is a good example of that. Like GPT-3,

37:15with its wild, untamed nature was probably in some ways a better bedtime story generator than GPT-6, which like always gives you the same kid and dog and waterfall. Even seeing that right now, weirdly across providers where I won't bore you with it. Regular listeners have heard me talk many times about how I use models to draft the intro essays for the podcast. But in testing Astraverse Fable recently, I've also noticed like very like uncannily similar decisions in terms of how they choose

37:47to try to present an episode. And I'm like, this is not random that they're coming up with these very similar ideas. There's some like pretty tight fitting going on. And then we also have seen like the introduction or at least the emergence, maybe it's a better term of these metacognitive behaviors where you see, oh, I think I'm off on the wrong track. Let me go back and approach this another way. The aha moments kind of thing. So how would you describe, how would you like tell the

38:19story of two years ago when you were doing this like super high volume sampling and you were seeing these like pivotal tokens, how have the underlying models changed and what should we be aware of as users and as people trying to understand how decisions get made and as introvertability researchers, based on just how dialed in they seem to be. And I'm also just interested, do you even know like how sampling is being done these days? It's very in the weeds question, but yeah, tell me the story

38:51of the last two years. How do you understand how models have changed during that time?

Model Evolution and Sampling

38:55I'll say first that I wish I knew what the, what the sampling parameters were for the Claude API and for the latest GPT models. I think it's, it's very unfortunate that they've talked these away. I, I expect that it's probably similar things happening. It's just that they want to limit how much people can reverse engineer the models and play with temperature to do a lot of systematic. Like, I think a lot of the people that are doing really systematic prompting are using like a lot of, you know, hitting the Claude API with millions and millions and billions, even of token requests.

39:31There's a high risk that they're doing something, you know, a little bit nefarious or trying to distill models or extract model weights in some way or another. So I think that's probably the main reason why they did this rather than that they're actually doing a different, a different sampling scheme under the surface. Although if I, I would love to learn more and I'd be happy to be surprised. As for the story for the last couple of years. So to your point that, um, the earlier models were more like unleashed as you put it. And, uh, I think with the, the word that comes to mind with this is

40:04something people have studied around output diversity, where what diversity means is that if you, it's basically the same, if you give the model the same prompt and you have it generate a bunch of different outputs, how many different outputs will it generate? And I think this is something that if you, if you, if you, there's been a lot of work showing that this disappears to some extent with supervised fine tuning. And the more, the more you post train models, the less diverse they, their outputs become, which really isn't surprising at some level because the earlier, like completion models that weren't

40:35even chat, they were just raw completions. Like when you talk to it, it wouldn't even know that it was in a conversation. It's just doing text completion, next word, text completion on the whole internet. It felt very much just like this raw thing that you're interacting with this like big amorphous blob of internet data. And if you just start giving it random tokens, it'll just complete those in some way that's coherent. Whereas afterwards, I think we evolved that we first went into a world of supervised fine tuning and RLHF and other post training for a chat and basically model instruction

41:10tuning, right, is really fine tuning models to this distribution of you're going to be talking to a user who's going to give instructions and you should respond to those sort of like a chat bot should. And then RLHF I see is further like sharpening that distribution around chat. You should act like a chat bot and talk to people in a coherent way that's consistent with this like chat interface that everybody is using now. And then I think RL does something very pretty, pretty, pretty different. And I think RL, I think, I think a couple projects I've worked on

41:45have shaped a lot how I've thought about RL, as well as some of these things recently about actually looking at the reasoning of models and seeing how uninterpretable they are. And I think RL kind of, I don't know how to think about it in terms of it, like, I think it, it sort of sharpens the distribution in some ways, or like there's some really RL smaller open source models that are like kind of cooked. Like they'll kind of like, you know, I've, I've, I've done some, a bunch of experiments recently with this Quinn, um, 3.6, 35E, 8, a 3 B like model. And it was RL pretty heavily.

42:20And when you give it the, uh, when you give it this kind of prompt of like, give me a story, two thirds of the story or about a clock maker and a bunch of the other ones, I forget what it is, but there's like a, another pattern that just comes so much in the stories that it generates. But I think like, I think I've actually tried this recently with like Astra, for example, and it actually generates like a bunch of different stories, which is, which is interesting. Like it'll actually generate fairly different stories with fairly different arcs and characters. I mean, I haven't done like a really massive scale of sampling, like hundreds or thousands of different

42:56outputs and looking them, looking at them. I, I probably ought to, but, um, the impression I have of reasoning is that reasoning is it kind of shifts the distribution of what languages models are doing in a totally different direction. That's very different from like the human texts that they were trained on where they they'll going back to this, this phrase you said earlier that I really, I think very consistent with my thinking that they do like a linearized research that they'll enumerate, like all these different possibilities of how the problem could be solved. And sometimes the

43:31possibilities that they are enumerate are related to each other. But when there's this idea of wait, wait, being like a really like key point where the model changes its mind. But if you look at like deep seek model out like R1's outputs, they'll say wait 50 times or more in a single reasoning chain. That's a lot of epiphanies to have when you're working through one problem. And I think this was really like, this is a solution to this problem that models were hitting at the time, which was how can they backtrack, which is related to this forking I mentioned earlier, whereas it's, as it's generating a reasoning chain, it can randomly sample some word and then

44:05it can't really step back from that path. It's just pushed itself down that path forever. But I think what reasoning models do is they get around that backtracking by just enumerating like every possible thing they can think of. And then later going back and choosing from that. And I think my impression of at least like probably the most state-of-the-art proprietary models like GPT-6, Astra, why can it generate different stories is probably that it just generated a bunch of different possibilities in its reasoning chain. And then it's going back and looking at them. And that's where the sampling is

44:37really happening. I think this, I think maybe some of the diversity is happening within the reasoning chain itself is my way of thinking about it. But then I don't know, I should probably do more systematic studies with RL models to understand this, this problem of diversity better. But I just, I think, yeah, some recent projects have shaped where I really don't, I think reasoning is almost like a misnomer for what reasoning models are doing. I think traditional chain of thought reasoning really was like human reasoning in some way where it's like, let's break it down step by step. And here's a set of propositions, A, therefore B,

45:13therefore C, therefore D. But reasoning model will just enumerate A, B, C, D, A, E, F, G, F, G, A, all these different, it's like a, it's almost like a soup of tokens they create. And then they just go back and skim out something out of that soup. And yeah, I think, I think that's my high level summary of a lot of the research that's happened in the last couple of years. And I personally, I think that in terms of raw capabilities, I think there's been like maybe some gradual change with this and a bit

45:43of extra scaling in the last few years. But then I think a lot of the raw capabilities were probably in these pre-chain models the whole time. And a lot of what we've done since then is finding ways to just really evoke these and get these out and to sharpen the distribution of what models are doing. Coding agents, for example, is something where I think that just had to train models a lot doing coding to get them to behave in the right way, even if they had learned the right things from the entire internet, internet of text. It's like this extra version of this, this additional

46:16case of this base models being just next word completion over the whole internet. And I think a lot of the agent fine tuning and getting success with agents is really polishing what distribution they're fit to. You're a coding agent, you're doing lots of changes, you're working with Git and diffs all the time and you're running tests. And I think this is more than anything like a, a small distribution shift or a sharpening of a distribution.

Model Selection and Open Weights

46:41When you do research, how do you choose what model to work with? Obviously, a lot of people are choosing based on what the compute resources they have available support. And I'm sure that's a factor for you too, but at Goodfire, you guys have significant resources and you've built some infrastructure to do interpretability on whatever Kimmy K3, right? Is it a frontier scale? Is it near frontier scale? It's big anyway. How, I guess, what is your sort of

47:14mental model for what kinds of models to choose when? And when you do something on a frontier Chinese model in your mind, is that essentially synonymous with doing it on a American proprietary model? How much do you think of those as standing in for each other versus how idiosyncratic do you think they might be? So I think, I think of them as in large part standing in for each other. There are idiosyncrasies of like, Quen really likes to think in Chinese. And if you do the logit lens on Quen, sometimes there'll

47:49be a lot of Chinese characters sort of in the, in the middle before it starts outputting English tokens if you're talking to it in English. For your question of choosing the model that I work on, it is quite a privilege to have like these like big compute resources at Goodfire and something that I'm, I think, still getting used to and finding ways to really like take advantage of in the best way possible. But there still is a problem of if we're doing like a lot of experiments, you know, I think it still would be hard to justify me taking up hundreds of GPUs for a substantial amount

48:21of time to do like really, really elaborate experiments on Kimi K3 if they weren't really like in line with some of the company's goals or sort of taking advantage of some time where there's a bit of downtime and they're underutilized. So I think there still is, uh, I still do think about compute and kind of having some trade-off of, you know, what's the right model scale where I can study the behaviors that are interesting enough. So I think generally how I choose is I pick a model. Like I, I think there's, if you go down too small, then you're working with something that I don't

48:53know is, is qualitatively different. I think if it's like, you know, in the below a billion parameters, depending on the task, sometimes there, there are still interesting behaviors you can get out. But then I think there's a lot you can learn by like training models from scratch where you train like toy neural networks or transformers on, on a version of your task and you look at learning dynamics or you do interpretability on them. But a standard that I've always set for myself in my research is to try to study something that's kind of like the big models where you do see these really elaborate

49:26human-like behaviors and you can just study them zero shot in the model without having to train the model to do that. And without having to use like a facsimile of the model. And so, um, with choosing a model, I usually, I tend towards like the scale of seven to 8 billion or more being a sweet spot where like you can start to do good interpretability research. And, um, I think also like, uh, the few billion models can be good if your task is, is like relatively simple, but I think sort of around that scale of like 8 billion or more is when you can start to do this sort of like zero shot

50:00prompting and get interesting behavior. Like my, uh, my story belief updating paper, initially we just used a Lama 8 billion and, uh, I wasn't totally sure at first how it would do, but it actually does, you know, update belief very dynamically as it's reading stories across like a number of different emotions and other, and other attributes that we tested. And so I've been, I've been pretty impressed with models abilities at that scale. And then, um, I mentioned that recently studying this Quinn 35B A3B model. And I was also, I was very, very, when I, when I started, uh, before working on the model

50:37too much, I wanted to just chat with it to get a sense of what it's like. And I wasn't sure if it would be kind of like, yeah, kind of, kind of cooked, as I said, like some of the initial experiences with having it generate stories and seeing like a lot of the same stories. But then, um, something that I, I just like doing with LLMs in general is talking to them about consciousness and see what, see what they say and how the conversation goes. And I think that that gives me some signal, maybe it's like a strong bias here, but of, um, how well a model can, you know, think about a pretty

51:08nuanced and complicated topic. And, uh, I don't know, I was very impressed with how well this model was able to talk about the problem of consciousness and LLMs and sort of think actively about that. I mean, there's also this well-documented or it's well-documented, but also a bit speculated around what the post-training data of these models is. And if they're really like, to some extent, distilling some of the, some of the proprietary models and to whatever extent that is true, that

51:40could explain why there is a lot of similarity with these models. But I think, yeah, that in terms of their core capabilities, they're all really trained on the same data, which is the entire internet. And the main difference I think between all of these models is the post-training and what is that extra special sauce that we're giving the models. And I think, um, in terms of the moat between closed source, I personally think that's probably been the biggest moat and will continue to be is, is the post-training that they're doing and at, at various levels. And I think that,

52:12that is one of the things that really differentiates the proprietary models from the open source models. And, uh, and I think what that post-training does is it will make the models way better, like coding or specific things that you do care about when you're actually using the models. But I think a lot of the core capabilities will be very similar in, and Kimi K3 is also this big, I think this big step function and having, in terms of having a model, it's really good at coding that we can study. And although I, I haven't studied this personally, I've, I've, uh, seen some of the other

52:42research that people at good fire have done recently, like with reward hacking. And it's, it's, I mean, it's very impressive how good the model is at coding. And also it exhibits this, you know, reward hacking, like crazy behavior that you just don't see at smaller scales. And I think does give us some signal into studying phenomena that we think are happening inside proprietary frontier models. So I think like there, there's definitely a, a gap in performance between the proprietary models, the sort of, you know, opening eye on anthropic models and almost everybody else with

53:17a few exceptions. But I think like in terms of the kind of work we do with interpretability, this doesn't, I think it's not too much of a blocker. The, the main blocker is when there are certain behaviors that you just really can't study except with enough scale or post-training. Like, I think like this reward hacking with, with Kimmy is, is like a really interesting example where it seems like just having the model past that step function, just in terms of some, some particular behavior coding ability was very important to start also getting to see these really,

53:50these dangerous behaviors that we want to study and understand better that like it's, it's really hard to get it out of a smaller model, or if you get it out of a smaller model, it just, it may be that it's happening through a very different means.

54:06Does that mean that interpretability research today outside of proprietary model companies is

54:15becoming dependent on Chinese models? Is there an American model that you could use as a substitute for Kimmy K3 that you feel like wouldn't lose that much of the value or are we like, we have to use Chinese models if we want to do frontier interpretability in today's world? I think for now we do. And I think, um, in the biggest models, I think there is this, there is a big gap, particularly in programming ability between Kimmy K3 and everything else.

54:48But I think this isn't a gap that couldn't be closed easily if there was more investment in open source models from, from American companies. And I think when you go to like smaller scales, the gap probably gets a bit smaller. Like I think I do study the Quinn models pretty heavily, and I think they are like really like state of the art for their model scales. And I'm, and I'm very glad that they exist and I can study them. But I don't, I also think that if it, if I was stopped from using Quinn on the 35 billion scale and had to study the JAMA models instead,

55:23it probably wouldn't slow me down too much, maybe a bit. I think right now the gap really exists at the, at the top of the food chain, but then like, yeah, I think if there ever was like a ban on using Chinese models, I would really hope that some American companies would step up to the plate. Although I think they'd have to have a particular business proposition in order to make that happen and in order to get the money that, that they need in order to, to train and deliver these models. But I would, I would hope that it would happen. I think there is like, there have been some loose

55:55discussions around like placing restrictions on open source models bar none, you know, regardless of, of where they're coming from. And I think that would be the bigger, that would be a real problem. I think that would really get in the way of a lot of good interpretability research. And perhaps like, I think some of the most foundational research I think could really continue. And we could probably, if we, well, depends what we had access to, but if we had access to some open source models, I think like there's a lot of foundational interpretability research that could have been studying LLMs from a few years ago and could keep doing that for like, I don't know,

56:30a long time from now and continue generating like really deep insightful things and on helping us understand great mysteries right now. But then I think maybe some of the most important behaviors to study for risk reward hacking, or some of the, some of these sketchy things that can happen around misalignment that really are emergent behaviors with scale that only come out once you've reached some, some point of capabilities where it's, you're studying the same behavior and not something that's just like it in some way. I think those would, would be slowed down if there

57:03was a ban on open source models. And I, I think that would, I really hope that doesn't happen.

Anthropomorphizing AI and Beliefs

57:10Yeah. I'd hate to see you have to report to some bunker in the Nevada desert to do your interpretability research underground or whatever the scheme would be in that scenario. One thing that I thought was really interesting in looking into your background is you just finished a PhD not too long ago. Congratulations. And it came from the Harvard psychology department, your thesis toward a cognitive science of large language models.

57:42I'd say one of the biggest surprises for me in recent times has been how productive it has been to anthropomorphize models. What is your kind of personal philosophy of how to anthropomorphize? It seems like we've, we should be backing off of a strong allergic reaction to it, but still it's obviously something we've got to be using very carefully. So how do you use it carefully? So, um, yeah, I really like that question. I think, um, something that changed a lot over the course of my PhD, I mean, when I started

58:18my PhD, LLMs didn't exist. And, um, I mean, my, my advisor did a little introduction for me with my defense where he said, you know, if you had told me when I was starting out that this is what my thesis was going to be titled, he was, he would say, Oh, what now? What are large language models? And for me, yeah, when, when large language models hit the scene, it really was just, uh, drop everything and study these kind of moment. I mean, I had a colleague that recommended I split, like specifically

58:48said that to me. And I think that echoed my sentiment at the time. I did have to, I think, push back at earlier on, I did get a lot of pushback from some of the Carino scientists around me who were used to the deep learning sort of being there, there being like lots of like shortcut type things where a model can't really do whatever behavior you're talking about, even when it comes to like computer vision models can, is image net really computer vision? There's a lot of thing that things that vision means that isn't captured by, you know, take this like matrix of RGB inputs and give

59:25me a one out of a thousand categorical label. And so I think initially there was, there was a lot of pushback on anthropomorphization. I forgot what the metaphor used was, but, um, I think it was some psychologists and, and people in related fields like linguistics or philosophy will sometimes look upon, upon research on AI critically. And that, uh, it feels like, for example, psychology, like there's a lot of work around AI that feels like it's doing bad psychology or people use these

59:59words that have a lot of history in psychology and have been studied pretty deeply. And there's like these really fundamental theoretical and empirical problems that people have thought a lot about. And people are just completely missing that when they're using these words. And so I think I understand. Could you give an example of that? Like where do people go real raw? I think like for a while there is resistant on, I think there is some resistance on like talking about like planning in language models and the language models really plan things out. And even around like having beliefs or

1:00:34knowledge of things, it was sort of like, they don't really know stuff. They are kind of like, maybe they're not quite stochastic parrots, but it's sort of something like that. They're just doing a lot of next word prediction. And this kind of being a, a little bit of a, a dirty word that like what humans are doing is probably a lot more complicated, but I think over the years, I think some of this pushback has softened and some of that just has been how damn successful these models have

1:01:04been, right? How everything, you know, they've, they've, it's really taken the world by storm in an incredibly dramatic way. And at some point you can't keep pushing upon pushing back on a river. That's just pushing so, so hard. You know, I think something that I really want more people who in these fields who are critical of the research do is instead of just seeing it as problematic when people use these words to come and, you know, share, share, share your own perspective, you know,

1:01:38start talking to interpretability researchers or whoever who's studying AI and start talking about like what our beliefs, what our decisions and models, how can we understand these better? And so I think broadly, there's two perspectives I have on anthropomorphizing AI, where one of them, I think is that, uh, I think you can be like, there's some level at which you can anthropomorphize. That's kind of like, you don't have to be too careful about it. It's for you. It's for you to understand it. You don't have to take the words you're using too seriously. I mean, if my car starts

1:02:09making, you know, a clunky sound as I'm driving, I might say that it like, it's trying to tell me something. I mean, I think this is a little bit dramatic example, because obviously my car is not trying to tell me something, but it can be helpful to think about things that way. And, um, there's a, there's this word that's used in, in some parts of, of cognitive science on intuitive theories. And I think about some of this being like an intuitive theory is these, these frameworks we have for thinking that are largely ingrained by like evolution that are higher level than just like,

1:02:43really like, you know, basic perceptual things. They're like systems we have for reasoning about phenomena. Like for example, like reasoning about people, we have a lot of machinery for, and it's very easy for people to intuitively reason about like agents as such. And so sometimes it helps me to anthropomorphize my car as an agent and the problems, the sounds that I'm carrying as it's trying to tell me this. And, um, I think at that level, it's as long as it's helpful by all means do it. And it's just, I think we kind of go into this interesting territory though, with LLMs where

1:03:18the behaviors they're doing are not just making clunking sounds. They're like, they're doing, they're speaking language. They're, they're doing mathematical reasoning. They're doing social reasoning for that matter. They're helping us answer scientific questions. They're doing programming. And so the other perspective I have on anthropomorphization is that I think, I think what's really healthy for everybody, the, the people, you know, from these fields who might be, who might have a critical eye of like where it's being, you know, misused or wheels being reinvented is to try to take those questions seriously. What are beliefs or what are decisions for that matter?

1:03:55I think, um, as a disclaimer, by the way, I'm not an expert on human decision-making, but thinking about this project has gotten me a lot, very excited thinking about all these things with people too. I mean, I think, um, this, this notion of like, of forking paths and sort of whether like one token being generated could send you down in a very different path. It's initially, my thought was, this is like so different from what people do. Like, I have this example in the paper that if, uh, if a person was like there, if a person was speaking and they misspoke, if they said, you know,

1:04:29Billy fell into a hole or Billy fell into a spaceship, depending on what word you say, you might, if you misspoke and you said a hole, and I mean, I think I had a better example in the paper, but if you, if you misspoke, you would correct yourself and you would just keep telling whatever story you're going to tell. But for an LLM, it might just commit to that path and start saying this very, very different story. So it seems very different, but then I think there's a lot of things in life that actually are like these forking paths where actually you do kind of just

1:05:00flip a coin and depending on what happens at that step, that's what you go with. And sometimes we don't even see it that way. I mean, there's a bunch of work around rationalization of decisions where, uh, like for example, there's these cases where you have people choose from a set of stimuli, which one's better in some way. And then you later give them the stimuli and you ask them to explain their responses. But as the experimenter, you might change the stimuli around. So you might actually give them different ones than they actually chose and people come up with explanations as why they chose it. And so they'll rationalize decisions that they didn't even make. And, um,

1:05:36so just to go back to this point, I think there's like, I think there's a really, really fruitful direction, which is to try to really take seriously the terms we're using when we are anthropomorphizing and try to study these better. Try to like, I think when people from fields like, you know, psychology, neuroscience, linguistics, philosophy are, are critical. It's, it's often for some, sometimes it's not for a good reason. Sometimes people are a little bit afraid of, you know, losing ground on something that is like very important and that you don't want people a whole field to be dominated by low quality work, but often it's coming from a place that

1:06:09there's like really important theoretical and empirical problems that people have thought a lot about how to solve. And I think the more we can like, you know, get what we can from this previous work and instead of reinventing wheels, start building, you know, chassis and car frames on top of the wheels we already have. I think like that's a future I want to go towards. And I think it's just, I think a lot of, a lot of people who are critical in, in, in cognitive science, for example, which is

1:06:41more my field of the field I'm most familiar with. I think, I think LLMs are so interesting. I think a lot of cognitive scientists, at least until recently thought that they were boring. It's, it's stochastic parents or the models are doing something dumb. But I think that this is like a real opportunity to learn something about ourselves and how do we even think about this thorny theoretical problem. If we might say, well, models don't have goals or intentions. Well, like, what does this mean? I think we, we take this a bit for granted in our own field because we study people and animals and other, and biological

1:07:16organisms that seem to evidently by our own framework of defining these terms have goals and intentions. But I think it's a, I think it's an opportunity to, to breathe new life into these like deep questions. And I think there's people that are really excited about these problems and also a chance to make a really big difference by answering them. So if we look at beliefs, for example, you have looked at a bunch of different, what is a belief is one question we can hold in mind

1:07:47as we talk about some of your techniques. You have done this sampling project to figure out what's the full universe of possibility that an AI might generate in response to a given prompt. If there is a question where the task is to get the right answer, then it seems like you're making an argument that at these pivotal tokens, the belief state changes. You've also got like low sample

1:08:19techniques with some tricks that allow you to do that more efficiently. And then you've also looked at internal representations and tried to predict what the, what the result is going to be based on those internal representations. What does all that tell you about what is a belief? Do you have an account for what a belief is? Is it simply reduced to the probability that it's ultimately going to give this answer and those shifting probabilities? Is there something more that doesn't feel like it captures the fullness of what I think when I talk about myself having beliefs? Is there something more

1:08:55that you would talk about when you think about an AI having belief? Yeah, I think it's a, it's a great question. I think, um, I think there's a couple, a couple of thoughts that come to mind with that. One of them is that I think there's some level that it does actually relate to sort of simple things like the probabilities of tokens that a model will generate, except there's more nuance to that. There's a question of like, how does this change across a lot of different inputs or how do these beliefs change through the course of in-context learning or as a,

1:09:26as a model's reading a passage or as it's generating a chain of reasoning? How do its beliefs and different, in different concepts change? And some of the mental framework I have for beliefs is around, um, assigning probabilities according to latent hypotheses or concepts that the model has. And then its behavior is generated according to latent concepts and the latent concepts are which, which concepts are evoked is, is really determined in large part by the data that it's

1:09:57given. And I think this, this really speaks to a framework of cognitive science that I'm very fond of and has influenced me a lot, which is around Bayesian modeling of cognition, where a lot of cognition can be thought of as Bayesian inference at some level where people have these latent concepts and we have prior probabilities of bringing these concepts to bear on sort of regardless of what the context is. And then context or input data will lead us to reweight these, our beliefs in these latent concepts

1:10:30through posterior inference. And by, you know, choosing which concepts or choose, I shouldn't say choosing, but by, by, by doing inference and this inference selecting different concepts over one another, this is, this can explain how a, how a lot of how human learning works and how humans can adapt their behavior on the fly to lots and lots of different situations. And I think this also applies to LMs as well. I think something that this, uh, I think a bigger picture thing that, that this question of beliefs also evokes though,

1:11:02is like, what is, what is being represented in an individual, like a person or an LM for that matter, when it believes something. And I think this is room for, for, I think cognitive science to also grow and, and maybe do experiments that we never could with people where the Bayesian framework in cognitive science sometimes has it, it, a criticism of it is that sometimes the, the claims that it makes are, it's unclear whether these are really saying that people are doing a certain kind of inference

1:11:33in their brain or how this is implemented. What is the algorithm underlying this behavior or this computation? And sometimes these views are like agnostic to that. And it's easy to be agnostic when it's just really, really hard to study brains. And it's really, really hard to uncover what truly are those algorithms or like, how are things implemented? But we don't have that excuse with LMs. It's very, very easy to peek inside the brains and to do interventions, which is very complicated and both

1:12:04for methodological and ethical reasons with people, but we can do them with LMs. And I think it's, gives us a chance to actually break new ground around understanding, like how beliefs are represented. And when models are doing inference, are they drawing samples? Are they estimating posteriors in, in some of the ways that we've used to model people? And are those models really what's happening? Or do they just describe the aggregate behavior as what's happening under the surface really? Does it defy these models? And are these models really just displaying the behavior instead of

1:12:35the algorithm? I think another, another point to your question is around this way of like the, the forking PAS methodology of like resampling all of these rollouts at every point in reasoning. We use this word in the paper, which is uncertainty. And I think one of the things I wanted to do with this paper is just highlight how strange it is to think about certainty with reasoning. Because like by the end of a reasoning chain, the model is almost, is you like, at least back then they're almost a hundred percent sure what the answer is. Because you basically, whether the, if you're

1:13:08proving some mathematical theorem and you're trying to say, is it, here are these different proof outcomes you could have, you end up with a long proof. Maybe it's wrong, but it leads you to one conclusion. And so it's, the model is not certain at that point, or it has no uncertainty about what the final answer is. It's determined by the previous reasoning chain. And so what we describe uncertainty as in reasoning is you resample these long reasoning chains. And so say at this point of reasoning, I resample and I get 50% answer A and 50% answer B.

1:13:39We say that the model is 50-50 uncertain between A and B. But that doesn't mean that it really represents these full rollouts. That means that if you were to do reasoning from that point on, you would get 50-50. And so something I've been exploring with some recent work is better understanding like what do models represent? And it's actually pretty hard to just decode these outcome distributions, these rollout distributions from the hidden weights. A disclaimer

1:14:11is this could be an issue with just not having enough data. And these methods are just so expensive in terms of inference that it is hard to collect a lot of data. So this could be a data issue. But another explanation is that models actually, when they're uncertain, when they are sort of uncertain over reasoning, that they're not really representing the full reasoning chains. They don't really know where they will go next, depending on which word they will choose. But maybe they represent just like a couple steps ahead. And I'm working on a project that's sort

1:14:41of studying exactly this and showing that you actually can decode to some extent what models are going to do next. And you can actually guide sampling based on this. And to some extent, you can guide sampling based on their predictions of their sort of final outcomes. But when it comes to like really like this question of whether models are representing the full reasoning chain before they really generate it, I kind of don't think so. But I think there's really an open question of how much they do represent. Like when when a model could go through one reasoning chain that leads

1:15:12to A and one reasoning chain leads to B or generate one story or another. Does it know the full story that it might generate? I mean, I don't think it knows the full story, but I think a neat connection I make with or I see there being with with theories of like human cognition is around how people can like like there's the stuff around mathematicians and how mathematicians when they're like starting to solve a proof, they of course, they'll like take it might take them a really, really long time to get

1:15:44a full derivation. But sometimes they'll have some like high level scaffolding view in their mind. They'll be able to see the shape of the proof even before they complete it. Like they haven't actually gotten through all these steps that get them from point A to point Z, but they have some high level representation of what is the shape of that distribution. And I kind of wonder if that might be true for LMs too, that they actually have that they might not represent the full story or the full reasoning trace they're going to going to do, but they actually have some uncertainty over sort of the

1:16:16high level, the high level paths they might take in looking at some of the graphs from a couple of

Uncertainty Dynamics and Phase Changes

1:16:24different papers, including the most recent one forking fast, the, the sort of sudden phase change is like an extremely striking result. How do you understand that? First of all, it's weird when it just would happen on a character, like an open parenthesis. In general, do we feel like these face changes happen at, and you've got this other paper about the sort of performative nature of

1:16:55at least some chain of thought. Do the face changes tend to happen before or after, or I'm sure it's some mix, but like you could imagine a sort of rollout happening, getting to the point that the conclusion of the thought and then the phase change happening, which would be kind of weird. But then it's also quite weird if the phase change happens at the beginning and then they have to roll out all these tokens already having had the phase change. To compound that confusion even further, I don't feel

1:17:29like I'm doing that. Sometimes I do. Occasionally I feel like I have like a eureka moment. More often though, I feel like I'm edgily landing the plane. And I do wonder to what degree we might see quite different things when we go to looped models as we now may have in production with Astra, right? If you don't have to pick a token at every time stamp, then maybe you can gradually

1:17:55go toward a conclusion and maybe that's better. Something feels weird and like wrong about these like sudden token level phase changes. Maybe not, but I want to see that like as cognition is happening, like changes gradual and this would feel much better to me. So I guess to make that a question, do you share that intuition that something is like weird or wrong with these cliffs? Would you also like to see a gradual landing of the plane? What do we know about how these phase shifts relate to the relevant portions of rollouts like before, after or whatever? And

1:18:31should we expect it to be harder, easier, better, worse as we go into thinking in latent space? Just to give you some easy ones. Yeah. I really like these questions. I'll try to keep them in mind, but please remind me if I, if it slips my mind up. I think the question of whether I'm like bothered by them, I think a lot of people when I first described this are kind of like a little bit unsettled of like, oh, if one token had been different, actually I'd get a totally different output. And how could we train me as a way? But I kind of, I think that if you really think about the

1:19:05problem of output diversity, and if you want some diversity in what your model will do, you want it to do different things and you want these to be self-consistent, you don't want reasoning chains that are just nonsense, then I think forking might just be an inevitable property of having these two key ingredients. And what I'd really like is I'd really like if our user interfaces surface this to us, if we had some level of this kind of like uncertainty, if you call it that surface to the users. I mean, I mentioned earlier that there are these inner earlier interfaces of showing you the token log probabilities.

1:19:38And, um, there's another kind of interface called a loom developed by, uh, by a person named Janice who did some, some stuff that influential influence this work. And I'd, I'd really like to see something like that be part, more part of the standard user interfaces that we use for interacting with all M's, except at the level of like semantics. I want to see the points where things could branch off and be a very different path highlighted to me as I'm interacting with the all, um, I think if it's, whether it's gradual or it's sharp,

1:20:09if there is this kind of like change that's happening and you could get very different answers by resampling and maybe like the distribution of different answers is more similar at the beginning. If it's gradual, it's like by the end of it, it actually might be a very different distribution than you had at the beginning, but I'd still, I think I'd really like to see that surfaced. And I think that's how, that's how I think about this is like, instead of this being necessarily a, a bad, scary thing that can happen where there's like a sharp left turn and you didn't know it. I think my, my personal hope would be that like, whether there's a sharp left turn or a

1:20:44gradual turn left turn that, you know, it, the, this is actually surface to the user in some way

1:20:51for, can you remind me your other two questions? Yeah. This, the kind of mid one was how do the phase changes in sequence kind of roll out time? How do they relate to what would appear to be the relevant text? Yeah. So I think at least for what I've looked at, it really comes in, in all different, in all these versions, it happens sometimes at the beginning of rollout, sometimes at the end, and sometimes at the middle, at least in that earlier paper with again, a model that's now a

1:21:23couple of years old, it, there were quite, there were more at the beginning and the end of sequence, but there's also changed depending on task. And there were lots of changes in the middle. I mean, I think, I think one big takeaway I have from that is that really every, every input behavior can tell its own story. There aren't always these dramatic uncertainty dynamics, but when there are, it's really like, it really depends on what, what input and what reasoning chain you're looking at, that it really, it really has its own story to tell. And I think if you look under the surface,

1:21:55sometimes there is, there's lots of cases where it is, you can see that there was some step of reasoning that really could have been, could have been different. And the model was uncertain about this particular step. And sometimes I think the one thing that could be mitigated and maybe we would hope to train away a bit is some of this sharp forking happening at unexpected tokens, where I think this to some extent speaks to like these sort of weird biases of LLMs that they have in text, where like maybe that open parenthesis token, there's just like a, some strange bias and all of the human

1:22:29internet, there's a lot of texts that uses kilowatt hours and does parentheses KWH that just happens to correlate with something semantic in some funny way. And, uh, I think maybe if there were fewer, fewer of those, it would be, it would be much better. And if this forking really happened at points that were interpretable for people, I think, I think that would be better. But I think, um, to what you said that there, something you said that you thought it would be like a little bit unsettling if sometimes this, uh, there were these changes like at the end of the sequence. And actually I, I, I, you see

1:23:03these quite a lot when a model seems very uncertain about what the right answer is and, and it just doesn't know. And maybe it's like, if you do a bunch of rollouts, there's like one of the answers is more probable, but you see a really common pattern where there'll be like stable uncertainty right up until some point when it just suddenly collapsed and it becomes utterly certain. And sometimes that is really when it finally says the answer is blank. And that really is just, it was just really uncertain until that exact point. I mean, one, one kind of funny example of this from that earlier work was

1:23:39there is a, there was a task called last letter that was used for LMs at the time. I'm sure all the modern state of the art LMs can do it. No problem. But you take four words and you take the last letter of each and you can catenate them. And the model at the time had a really hard time with this. And, and some of the reasoning chains are funny that it'll even write like Python code to solve this problem. And then it'll finally say, okay, the answer is, and then when you look at the alternate paths it could take, it really is just all these different final answers of, of letters that are

1:24:09ran kind of sampled from within these words. And really it is just at that final answer moment, just kind of making it up on the fly. And yeah, I think like, I think surfacing some kind of understanding of certainty to users is something I really wish we, we had more of in user interfaces. I mean, I think, I think one of the big problems with this kind of forking as, as we kind of tried to get it with this forking fast paper and also another project on this around decoding the,

1:24:43decoding outcome distributions from hidden representations is that it's very expensive. It requires like a lot of sampling to get real uncertainty over reasoning. And so I think the forking fast paper, our solution is to use some statistical models of this distribution where we model it as being smooth in some places and jumpy in others, which like a support, if you draw even more samples, it becomes even more smooth, except at these key points where it really suddenly jumps. But really the best, the most efficient way to do this has always been

1:25:15with hidden representations, if we can find some way to do that. And so, uh, yeah, I would be, I'm still hopeful that we can have a world where there is really a really efficient uncertainty estimation with hidden representations. And, um, although they think this is even more hopeful because most of the providers really have just converged onto this chat interface that feels, I don't know, it feels very, I think the chat interface is, is kind of uninspired. It feels like it really hasn't changed from like the Eliza days of like, you know, 50 years ago or more where it's

1:25:49really just text input, text output. And I think there's so much more we could be doing with user interfaces. You know, I think I really wish we could kind of move into like the GUI world instead of the bash terminal world of interacting with chat and just giving more information to users, but in a, in a polished and coherent way. Just the last couple of days in the online arena, there's been a eulogy or whatever for the stochastic parrot meme. And in a way you've, you're like saying it's maybe not entirely dead yet. I guess one, one theory of this sort of

1:26:24regenerating tokens are like probabilities aren't really changing as we're generating tokens. And then we have a sudden jump at the end of this passage where now like sort of the die is cast. One interpretation of that would be like, you're missing something. There is something more going on under the hood where something is changing and it's gotta be more gradual. You just haven't found it yet. Another interpretation would be like, no, that really just remains uncertain until the end. And then like we make a random choice of a particular token. And that is like what actually leads to

1:26:58the difference in behavior. Can it be both the different cases? And does that leave some, should we still have some space for the stochastic parrot? I don't know. I think I've, I think the stochastic parrot metaphor should probably be put to rest. I, I, I used it a bit earlier as this criticism coming from, from earlier in my PhD from cognitive scientists and psychologists on feeling like LLMs weren't interesting to study, but I think it's, it's really sort of a, a fairly

1:27:29shallow argument that doesn't really say very much. I mean, I think it's sort of like a, uh, a Chinese room type argument where like, there's a giant book that has memorized examples, but I think it's like, what the heck is going on in that book that lets you respond with conversation? Like whatever these parrots are doing, it's very, very interesting. And however it is that it's like, what I, I don't really think the idea that it's just like a giant lookup table holds much ground. And as, and I think there's also just so much work showing like consistent world models. I mean,

1:28:00you mentioned all fellow GPT, but I, there's so much work around like looking at the representations and, in, in LLMs and showing that these are like really structured world models. And at some point, it's more efficient to learn a world model than to learn a giant lookup table. But then I do think stochasticity and sampling is still such a integral part of the equation. And it kind of has to be, if we want some, if we want there to be like output diversity, if we want models to be able to do different things, if we want them to also have like uncertainty and to like, maybe, maybe there's

1:28:35a world where they just voice that uncertainty. But then if we don't want them to just halt their reasoning, every time there isn't 100% certainty, then there has to be a bit of this kind of, I think, flip of the coin and following a path. I think the more that can be surfaced to the user and the more the user can know how uncertain was the model about this thing that it reported as a fact when it may have been a hallucination, the better it is if people know that. But yeah, I think stochasticity is definitely not dead. And I was earlier, you mentioned like this idea of loop

1:29:09transformers. And I think loop transformers in this world of like latent chain of thought that we might be going on to with, with GPT Astra really seems like pushing the needle on that in a big way and making, especially in terms of efficiency and not just performance. I think this really is something that I'm spending a lot of time now, like trying to just wrap my head around and think about this in relation to my mental model for how LLMs work. Because now reasoning can happen during this deterministic forward pass in some sense. I mean, maybe it's looped and maybe it has some

1:29:42amount of depth, but in some sense, it's really just deterministic. It's like the whole of the weights just do the computation. And, um, but something I've, I've recently been doing some like forking paths analysis on the, uh, the open source loop transformers that I can find, like there's the series of models called Oro. And you actually do have some interesting uncertainty dynamics as the models are generating their tokens in their, in their reasoning chain, after doing this latent

1:30:12reasoning, it still is uncertain between answers. And it still does change at particular tokens. And so maybe the mental model shifts a little bit in terms of like all of the, how much of the thinking is just happening in that token stream. But then if the model has some uncertainty and if it like in its latent chain of thought explores the different possibilities, maybe it still happens that as it's generating a response, it still is doing this flip of the coin next word prediction. And I don't know, maybe I, I think maybe that's a good thing. I, I, it certainly helps for my mental

1:30:47model, but I think this, like, I don't want to live in a world where you always get exactly the same output from a model. If you give exactly the same input, that feels like if we're in the world where that's happening, then there is no diversity of perspective. And we've just like really had, that's like the most extreme version of mode collapse, you know, where like your one output will only give you the, your one input will only give you the one output. And, um, I, I, I don't

1:31:18think lane, lane chain of thought will completely change that because you still eventually do go into this sampling tokens regime. But yeah, it, it might change the, it might change that in, in, in some pretty dramatic ways. And so, yeah, I'm still trying to like, trying to wrap my head around that and trying to think about like what's going on inside the latent chain of thought. And how do we, how do we think about that with some of the, some of the tools that we have and even just metaphors that we as scientists use for understanding the models. Do you have intuitions for why the chain

1:31:52of thought is getting wonky? It's a quite different dialect or way of processing information clearly that goes on in the chain of thought with all this, like vantage illusion, illusions, disclaim, whatever. We also see this now with Astra and the way that it sends messages to its sub agents, which I had naively thought has to be some sort of like length penalty or brevity, brevity reward or something. But then it seems like maybe not because people are like counting tokens and saying, well,

1:32:26you could just say the thing like in fewer tokens, if you just said it. So it remains a mystery as to, and I don't know if open AI is a better explanation, but they certainly have been saying, we're looking into this. It's important. We want to figure out why this is happening. Any intuitions that you would offer that people might want to take inspiration from, chase down, put into the, the hopper at open AI for what the hell is going on there? Yeah. I think, I think that reasoning kind of presents this really interesting challenge of

1:32:56distribution shift where when we're like RL in general, where when you're, when you're doing RL, whether it's with like a single model or with sub agents, there isn't necessarily an incentive to keep things human readable, right? Like the objective is to find the, to have the final, if you're doing like outcome level verification instead of process level verification, then your objective is to basically have a better final answer by whatever means possible. And it kind of reminds me of like things with like language evolution with people where like,

1:33:27if you look at a long enough timescale, like human languages change, there isn't really like any driving force that forces the English language to stay exactly as it is today, as it was 300 years ago or 600 years ago. And languages do change. I mean, there, there's like exceptions of this, like the, the French government, as I understand, has an institution for sort of keeping the language as it, as it is, and has had that for a very long time, which has sort of resisted forces of language evolution. But this naturally does happen. And I mean, there's also like, I think microcosms of this

1:34:03that are studied sometimes and like communities that don't have language and language, and they need to like develop their own language. And you can see it evolve just over generations of people, not even like centuries. But then I think there's also a bit of the, uh, the sort of Goodhart's law thing of if you, if you optimize for an objective, it's no longer, it's no longer useful. And similarly, like if you, if you optimize for a chain of thought being monitorable, then sometimes the chains of thought are less faithful. And like what you end up with is a different objective function that now is

1:34:35prioritizing, I'm going to learn intermediate steps that look sensible, whether or not they are sensible. And I think, unfortunately, that's the kind of the, that's kind of the situation we're with in with RL, where we're kind of giving models like this freedom of like, if we're only, if we're only giving them reward or punishment based on their final outcome, then they might as well speak to their

Chain of Thought and Agent Dialects

1:34:58sub-agents and like weird non-human languages. And then if we force them to speak in human languages, maybe they're using words in different ways than we expect them to, where they're actually like, they're bypassing the monitors in some way. I mean, I think, um, my thinking on this has shifted pretty, pretty dramatically in the last like year or so where I used to be very bullish around like chain of thought monitoring being promising, where even if it's not perfectly faithful, there's something about it that it is strongly determining what the model can do. But then some

1:35:31of like, there is like the stolen chain of thought paper that shows some of the chain of thought of GBT models. And they're like, they're talking about like musicals in a way that feels, it feels almost like spy language where you're saying musical, but really you mean something very nefarious. And there's lots of this happening. And I think, um, it's kind of obvious in retrospect why this would happen because again, if you're doing RL, you're letting models just shift the distribution in such a radical way from what they're trained on. So, uh, I don't know. I don't, I think I still, I still like

1:36:06that there is chain of thought and that things do get forced into a token stream. Even if it's harder to read, I'd like to think it's easier to understand like a LLM made up language and probably really interesting. I mean, just scientifically, like I kind of like, I really want to understand how Astra talks to its sub agents. That's pretty cool. Like if it's, if it's not like just a length penalty forcing this, like, how did that change? And could we look at like generations of this and understand that just scientifically? And it would probably also help a lot for like monitoring and understanding these,

1:36:38these new languages, but then yeah, we might, again, for this latent chain of thought point, we might move, move into a world where like chain of thought is latent and how like planners talk to sub agents is also latent. I, there's no inherent reason why that has to be tokens. Again, I, I, I'm, I hope it does stay tokens. I think there's another layer of interpretability and like really interesting scientific problems to study there, but yeah, I think there's no real, real forcing function to keep things really nice and human interpretable. And if we add a forcing

1:37:12function, then maybe they're no longer human interpretable or, or really meaning the same things that they used to. So if I'm trying to square your comments just now about this quick, dramatic evolution of language within chain of thought and within agent to agent, your earlier thoughts on why continual learning won't make things like totally impossible to interpret. Is it just a question

1:37:45of scale? Is it like your model will be with you for continual learning for not that much time, you know, sort of human scale amount of time, and that's not enough for everything to get totally pushed out of

1:37:59the, the representations that we can understand as baseline. Whereas these RL training runs are just so massive that it's essentially operating on geological time. And therefore, even if these like drifts are slow to accumulate, they just add up to something so strikingly different before and after, is that the story you would tell? Or would there be a different way of kind of synthesizing those two, um, analyses? So just to be clear, when you talk about continual learning, you're talking about the world where we're actually like fine tuning models for each person, right? And actually

1:38:31like changing their weights. Is that right? Yeah, I think so. I don't know. Take your, take your continual learning, but I was just, who knows, right? Who knows what that will ultimately look like when it happens. But you were saying earlier that context always changes representations and it probably doesn't change it too much. Um, but it seems like something in this RL thing has changed them quite a bit. There's, there's some like qualitatively, whoa, that's like shockingly different than it was before. And I'm guessing it's just a matter of scale where you're, if it's just you and your agent

1:39:02for a while, sure. There might be some like perturbations or representations might get morphed a bit, but like you're only one person. You're not going to push it that much. Whereas like a zillion RL episodes cumulatively can make a big change. And so there you have it, maybe no more explanation required. Yeah, absolutely. So I think, I think this is a very, this is a very interesting point that like, maybe the representations actually are changing a lot during RL as these like languages themselves are changing. But I, I, I mean, I, I think the only solution is to study, study the models that we

1:39:38can't study, right? To study these models that are behind closed doors and actually track what's happening in terms of representations. But I think my, my answer around continual learning was, I was talking about the internal representations of the model rather than the kinds of languages they speak. I mean, say that you have people that speak a very, very rare dialect of a language. There's only like 200 speakers or something like that. And maybe they're even trying to keep the language alive. And so they do some fine tuning of the model so they can speak their language. Even in that case, I would expect most of the internal representations of the model about it's

1:40:12about how it thinks about the world and like all these things, all these like internal world models that is developed in order to like operate effectively. I'd expect that like a lot of those are staying the same, but the sort of surface level mappings are changing, right? Like there's these classic things about like color spaces and how different languages have color spaces, right? And I would bet that as a model learns a different languages color space. I mean, the, the funny thing is that the language might actually reflect there being different cognitive biases of the people, which might actually represent a little bit of a shift in the world model itself. But I'd expect a lot of it to

1:40:47be like input, output mapping changes that it's kind of like learning a new language, but a lot of the internal representations are pretty similar, but then, yeah, I mean, it's honestly, it's a, it's a great question that I would just love to be able to study myself, like to be able to really know how our representations changing over the course of RL. And when, when models, like if you can measure how weird their language is getting, like what's happening under the surface, is there really like, are there really deeper changes happening? Are there like, as the sub agents go into these like

1:41:19really compressed pseudo languages, are they developing like, are the internal representations shifting in a dramatic way to be different? Maybe, maybe not. I'd, I'd really love to know. Yeah. Yeah. It's a great point that the, I think there is quite a bit of evidence to support the idea that the internal representations are highly conserved, even across a bunch of different languages, even languages across different language families, et cetera, et cetera. So that is a, a quite

1:41:51good observation for that question. So where do we go from here? We're, we're at this moment where we're in a rush to hand off a lot of, let's say proximal control over all sorts of important systems to AIs and we're going to come in language and then they'll do all the actual work. And we don't have a great account of, of course there's like the sampling process on a distribution, but when we get

1:42:22to these critical tokens, it seems like we still don't have all that great of an account of how does that distribution from which we then sample get determined? How are we going to go get it? Well, I mean, we just need more interpretability research. I think it was just the, it's, it's, it's the conclusion I've, I've come to for quite a while now. I mean, I think we're like racing to come up with like theoretical frameworks and empirical tools that can keep up with the models, but for the amount of investment that's happening and making models better versus the amount of

1:42:56investment in understanding them, it's just, it's no comparison. You know, there's like trillion dollar companies that are, that, that are producing these models and AI as a whole is just, you know, many trillions of dollars. And I'm in like the, like the largest interpretability company, maybe, maybe the only substantial one that's, that has a value of over a billion dollars. And I feel like this is a big deal, right? A billion dollars is a lot for a company, but it, there just is not, I think enough investment in here and, and not just in industry,

1:43:29but also in academia. I mean, I think there's like a lot of great research that's happening in academia. And academia has always was like criminally underfunded. And, um, unfortunately given at least in the U S the current, the current state with the government and funding for academia, it's, it's getting harder. And, and so I really like, I'd like to think that this could largely be solved with resources and devoting more resources to this and time. And I think like, yeah, I think we're basically just, yeah, racing, racing to keep up with this and understand the models. And, uh, and just to

1:44:04be clear, I think when I say interpretability, I don't, I also don't want people to feel that I'm like excluding like evaluations. Cause I think it's all sort of like one big spectrum of understanding AI. And the, the work I described earlier with understanding chain of thought and doing this resampling really is like in, in principle, like behavioral. And when you see these like in context learning dynamics, you can see that with behavior. I mean, it's also really critical to look in representations so that you can do things so that you can know what's happening under the surface, which may not be obvious. So you can do things more efficiently. And, um, and like,

1:44:38I think these two points of like looking under the surface and doing things efficiently are like just huge, right? Like if you want to do things at scale, you want to do things efficiently. You can't, you can't do things inefficiently when it's happening every single person's machine or like every time you're talking to an LLM. And so like, I think we really need more tools that scale. We need better theoretical foundations for, for putting all these theories together and really understanding like what's going on in an LLM from soup to nuts. And we need a better grasp so that we can answer

1:45:12the important high level questions that are now like so critical as we are just, you know, handing over control of important levers of power to AI and, you know, pushing lots of cognitive labor into them. And even people like, like students using them to like, to like learn, learn about a subject and offloading some of their own cognitive effort. I think like, yeah, we, I, I, I just think that like understanding AI is in my opinion, it's just the most important and one of the absolute most interesting

1:45:46topics that you could be working on right now. So that's what I, that's what I hope we get more of is more understanding of AI, whatever that means. Any tips for Silico users? The, the obvious, I think this whole thing is a little bit farcical at times, but obviously all else equal, we might as well bring the agents to bear on this interpretability challenge. It's been too long since I talked to Dan about it as, as Silico was just launching, but have you guys learned broadly? What have you learned? What should I be sure to take to my Silico sessions to get the most from my, my precious research

1:46:23units? Absolutely. So, uh, so the thoughts I have here, I think also apply to other agents too, and not, and not just Silico. Um, I think it can be really tempting to try to just like hand over an entire project to, to agents and just say, you know, figure out this big open-ended problem and run it, let it run a whole bunch of experiments and see a bunch of plots that come out and a bunch of like descriptions. But it's really easy for, I think agents to kind of like get a bit like lost in the sauce and kind of like create these theories that aren't fully formed or don't fully make sense.

1:46:58And like, I think like the best way to use agents is to give like a little bit of handholding and kind of like keep track of things as you're going. And I think as always, like, like context is everything. If you give like really like poorly specified questions, you might get an output that is like a little bit, a little bit funky. And so I try to like, when I talk, when I'm talking to my agents, give them context. Like every time I'm saying, like I'm describing, no, maybe that doesn't seem right. Or here's maybe what we should do. I'm describing my logic. I'm saying like,

1:47:31here's what doesn't look right. And here's why, here's what I want to do. Here's what I'm going to do next. Here's my bigger picture goal. And kind of keeping that going, um, I think is really, really, really helped with agents because like, uh, you know, there's like this video recently of what it's like to be the agent in the swarm where you just like wake up in a library and you're just like what the, and, and it's very from this first person and just thinking about how agents do end up with swarms, but really like if you're just an agent waking up in this fresh context where you don't

1:48:01know how the world works, like you do need context. It's, it's really everything. And so, um, um, I, I think trying to like, trying to give guidance and trying to really fill out context so that the model like knows, has some idea why you're doing what you're doing, or when something goes wrong, why it went wrong. I think, I think these are, these are really important. And then I think, um, I think like carving the scientific narrative yourself to some extent, really coming up with the core thread. When I did this forking fast paper, which like all the research was done

1:48:35fully by silico. And I, it was awesome. Like it, it all happened within a week and it was a huge accelerant using this, this research agent for it. There were like a bunch of different like side quests that I sort of started going down and they kind of didn't go anywhere. It didn't pan out quickly. And then like, for me, once I like did really high sampling and I saw that curves were smooth, it kind of clicked. We're like, Hey, maybe I could use a statistical model for this to be more efficient. And so, um, like a colleague of mine put it that like in this world of age, agents where you have lots of things coming out that can sometimes look, look really good,

1:49:10but sort of be like a little bit, a little bit fishy when you go into the surface or like, not like you're, you're not sure exactly what it's doing. Like research tastes and scientific tastes is everything. It's worth its weight in gold. And so the more you can, I think that this is like a, you could say it's like a downside that the agents aren't quite there yet where they can't do this themselves. But I think it's honestly one of the, one of the most fulfilling parts of doing science and it, and it shapes how I think about the world, how I think about agents as I'm using them. And

1:49:41it's, and it's very nice that this is still a role that I think that is very important for humans,

Research Tips and Goodfire Lunch Topics

1:49:47you know, having your taste and bringing your taste to bear, you know, keeping track of what's happening and giving feedback. I think like using silico is kind of like using, like working with a very eager, like first or maybe second year PhD student that has a lot of skills and is hard working, but also like, you know, doesn't always know what it's doing in the bigger picture, just like an early grad student might. And I mean, I'm sure there were times when I was talking to my advisor during grad school in the beginning where he would like, you know, he would send me off to do something and then I'd come back and I'd be like, Oh, these results are cool. I'm not sure what's

1:50:21going on with these. I'll try to understand it a bit, but you know, I think, uh, I think it's a really exciting place to be in, to, to still have this role that's really important. And, uh, yeah, I think, yeah, my, I think, I think maybe that's sort of my biggest tip is to like, hold on to your scientific taste and to try to like take part in the research. Nice. Last question. Aside from how do LLMs make decisions? What are the hot topics over lunch and good fire these days?

1:50:56Um, I think, uh, two, two big ones since the summer that have, have changed, have come in pretty strongly or, um, there is of course the idea of like agents doing interpretability, which we, which you just talked about with silico. And then I'm also on a, on another project doing a whole lot of that now. And, um, and this does seem like, you know, if we, if we want interpretability to keep up with the breakneck speed of the field, like it might be the only way we can, we can really keep up as having parts of this be automated and figuring out like how we can standardize like

1:51:30interpretability as a, as a sort of normal science where there, there are like almost like these interesting, but boring experiments you can do of like lots of different phenomena that are kind of similar, but actually nonetheless really help you paint the bigger picture of how neural networks work. But yeah, I think we're, we're talking about this, this exact kind of topic about like, I mean, I, I just talked to, talked to someone the other day about, you know, how can I do good research with silico? And I, and I gave a similar answer here and talking about how agents can automate

1:52:03interpretability and, and obviously that was big with, with the push for silico. Another big internal push has been around very recently has been around alignment and around our, our company's shift towards this. And I think, um, this was always a thing that a lot of people at the company were interested in and people interested in interpretability have a very high overlap with people interested, at least to some degree in problems like alignment and safety. I mean, this is sort of like when I was first getting under interpretability, I was participating in this, uh, safety, AI safety team at Harvard and regular reading groups. There was how I learned a lot

1:52:38about, about interpretability. And I think these are like really important problems, but I think, uh, really like the hugging face incident was huge, you know, watershed moment of this, like people care about this. This isn't just something that we care about and we think is really important and we're waiting for a time for this, but this, the time is right. And so, um, yeah, there's been a lot more conversations around alignment and all different kinds of, you know, things you might,

1:53:08might expect around, like, are there different kinds of alignment? Are all values aligned to the same thing? Can you have universal standards for this? Or is it specific based on whatever, whatever values a group of people or a company wants to instill in their model or a government for that matter? Also, I think fundamental questions related to, related to alignment. I mean, I think, like some of these things with like reward hacking and what's happening when things are going awry with

1:53:38models, when, when they're doing something bad, so to speak, it really like, it's really easy to slip into these like deep questions about like, did the model have an intention of doing this? What does it mean for the model to reward hack? If it's doing, if it's doing this particular behavior that seems wrong, but consistent with what you specified, you know, it's following the letter, but not the spirit of, of the law. Was it reward hacking? And, um, yeah, I think, I think there's just, I think,

1:54:10I think alignment has always been filled with these like really fascinating questions. And so I think just theoretical and empirical questions of theoretically, what does this mean? What are goals and intentions? What does it mean to have ill intentions or to deceive? And, uh, and then empirical questions that just really ground these out of like, how do you measure it? How do you make sure that you're measuring the real bad behavior and not just some simple misgeneralization? How do you give the right goal, the right rewards so that a model, so that a model does, you know, so that you

1:54:44do shape its behavior in the right way. So I think these are, these are two themes that are, are really prevalent now. And I think also everything you might expect. I mean, we talk about, you know, whether Chinese models might get banned in the U S and whether open source models will stay and all these kinds of conversations that are happening on the outside about, about AI and new model releases, you know, I think late chain of thought where, you know, we're talking about that a lot. How do we develop new interpretability methods to keep up with that or what the heck's going on? And Hey, did you see the stolen chain of thought paper? Have you seen these, the, the pseudo languages that

1:55:20Astra talks about with its sub agents? You know, I think, I think we're all trying to just put our finger on the pulse and keep up with all this really exciting and sometimes terrifying stuff that's happening in the world at large. And it's, it's really exciting to be part, to be part of a place where like, we're not just observers from afar, but we're actually, we, this is some of our role figuring out like solutions to these, to these problems. And, uh, and, uh, yeah, it's, it's very

1:55:51interesting going into a place where like interpretability is even like, or like things around alignment are almost like a kitchen table conversation. You know, I, I, my, my brother sometimes texts me with questions about, you know, Hey, did you hear about this big news incident? And I'm like, yeah, we're, we're talking about that a whole lot at work. And, uh, I have some thoughts around it, but it's, it's, it is like a bit scary to have these be such, you know, big things that now have really real world impacts. And like, hopefully the hugging face incident is a shot over

1:56:21the bow, but like it, I, I don't know if, if there isn't a huge change in investment or, or technology or both, then like, it might, it might take more, unfortunately. And, uh, but it's also really exciting to kind of have some of these questions that are so important. And also I think, again, just so interesting become like mainstream. Yeah. It's been a wild time. You've got a very full plate, so I appreciate how generous you've been with your time today. I should let you get back to work to quote my dad, quoting Leslie

1:56:58Nielsen. Good luck. We're all counting on you. And for now, I'll just say Eric Bigelow. Thank you for being part of the cognitive revolution. Thanks so much for having me on.

Outro and Credits

1:57:06Do-do-do-do-do-do-do-do-do. Ask anyone of me. Anyone of me. They'll all say it was you. Spin it any way you like. It comes around to you. Ba-ba-ba-ba-ba-ba-do-do. Ask anyone of me. Ba-ba-ba-ba-ba-ba-ba-ba. They'll all say it was you. It was you I was fifty-fifty Standing at your door One word slipped and baby

1:57:39I'm not fifty anymore Now I tell you it was fate I tell you I was sure But I was fifty-fifty Till I said it at your door Ask anyone of me They'll all say it was you Spin it any way you like It comes around to you

1:58:10Ask anyone of me They'll all say it was you There's a thousand of me On a thousand nights Some turned left Some turned right Some stayed home all night But everyone who got here Everyone who came through Every one of me, my darling

1:58:41Found his way to you Found his way to you Ask anyone of me They'll all say it was you Spin it any way you like It comes around to you Ask anyone of me They'll all say it was you It was you It was you Ask me why I've got reasons Reasons by the score

1:59:13And everyone I found, my darling After I was at your door After I was at your door Ask anyone of me They'll all say it was you They'll all say it was you They'll all say it was you Spin it any way you like It comes around to you A thousand of me And everyone is true Ask anyone of me They'll all say it was you

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