I now think it doesn't work easily, because the training on written language doesn't have enough examples of people explicitly stating their cognitive steps in applying System 2 reasoning.

The cognitive steps are still part of the hidden structure that generated the data. That GPT-4 level models are unable to capture them is not necessarily evidence that it's very hard. They've just breached the reading comprehension threshold, started to reliably understand most nuanced meaning directly given in the text.

Only in second half of 2024 there's now enough compute to start experimenting with scale significantly beyond GPT-4 level (with possible recent results still hidden within frontier labs). Before that there wasn't opportunity to see if something else starts appearing just after GPT-4 scale, so absence of such evidence isn't yet evidence of absence, that additional currently-absent capabilities aren't within easy reach. It's been 2 years at about the same scale of base models, but that isn't evidence that additional scale stops helping in crucial ways, as no experiments with significant additional scale have been performed in those 2 years.

That all seems pretty right to me. It continues to be difficult to fully define 'general reasoning', and my mental model of it continues to evolve, but I think of 'system 2 reasoning' as at least a partial synonym.

Humans clearly can do general reasoning. But it's not easy for us.

Agreed; not only are we very limited at it, but we often aren't doing it at all.

So here's my guess on whether LLMs reach general reasoning by pure scaling: it doesn't matter.

I agree that it may be possible to achieve it with scaffolding even if LLMs don't get there on their own; I'm just less certain of it.

For sure! At the same time, a) we've continued to see new ways of eliciting greater capability from the models we have, and b) o1 could (AFAIK) involve enough additional training compute to no longer be best thought of as 'the same model' (one possibility, although I haven't spent much time looking into what we know about o1: they may have started with a snapshot of the 4o base model, put it through additional pre-training, then done an arbitrary amount of RL on CoT). So I'm hesitant to think that 'based on 4o' sets a very strong limit on o1's capabilities.

Thanks!

It seems unsurprising to me that there are benchmarks o1-preview is bad at. I don't mean to suggest that it can do general reasoning in a highly consistent and correct way on arbitrarily hard problems^[1]; I expect that it still has the same sorts of reliability issues as other LLMs (though probably less often), and some of the same difficulty building and using internal models without inconsistency, and that there are also individual reasoning steps that are beyond its ability. My only claim here is that o1-preview knocks down the best evidence that I knew of that LLMs can't do general reasoning at all on novel problems.

I think that to many people that claim may just look obvious; of course LLMs are doing some degree of general reasoning. But the evidence against was strong enough that there was a reasonable possibility that what looked like general reasoning was still relatively shallow inference over a vast knowledge base. Not the full stochastic parrot view, but the claim that LLMs are much less general than they naively seem.

It's fascinatingly difficult to come up with unambiguous evidence that LLMs are doing true general reasoning! I hope that my upcoming project on whether LLMs can do scientific research on toy novel domains can help provide that evidence. It'll be interesting to see how many skeptics are convinced by that project or by the evidence shown in this post, and how many maintain their skepticism.

1. ^{^}

   And I don't expect that you hold that view either; your comment just inspired some clarification on my part.

Do you know of any material that goes into more detail on the RL pre-training of o1?

As far as I know OpenAI has been pretty cagey about how o1 was trained, but there seems to be a general belief that they took the approach they had described in 2023 in 'Improving mathematical reasoning with process supervision' (although I wouldn't think of that as pre-training).

It might well turn out to be a far harder problem than language-bound reasoning. You seem to have a different view and I'd be very interested in what underpins your conclusions.

I can at least gesture at some of what's shaping my model here:

• Roughly paraphrasing Ilya Sutskever (and Yudkowsky): in order to fully predict text, you have to understand the causal processes that created it; this includes human minds and the physical world that they live in.
• The same strategy of self-supervised token-prediction seems to work quite well to extend language models to multimodal abilities up to and including generating video that shows an understanding of physics. I'm told that it's doing pretty well for robots too, although I haven't followed that literature.
• We know that models which only see text nonetheless build internal world models like globes and  game boards.
• Proponents of the view that LLMs are just applying shallow statistical patterns to the regularities of language have made predictions based on that view that have failed repeatedly, such as the claim that no pure LLM would ever able to correctly complete Three plus five equals. Over and over we've seen predictions about what LLMs would never be able to do turn out to be false, usually not long thereafter (including the ones I mention in my post here). At a certain point that view just stops seeming very plausible.

I think your intuition here is one that's widely shared (and certainly seemed plausible to me for a while). But when we cash that out into concrete claims, those don't seem to hold up very well. If you have some ideas about specific limitations that LLMs couldn't overcome based on that intuition (ideally ones that we can get an answer to in the relatively near future), I'd be interested to hear them.

 As a small piece of feedback, I found it a bit frustrating that so much of your comment was links posted without clarification of what each one was, some of which were just quote-tweets of the others.

Logan Zollener made a claim referencing a Twitter post which claims that LLMs are mostly simple statistics/prediction machines:

Or rather that a particular experiment training simple video models on synthetic data showed that they generalized in ways different from what the researchers viewed as correct (eg given data which didn't specify, they generalized to thinking that an object was more likely to change shape than velocity).

I certainly agree that there are significant limitations to models' ability to generalize out of distribution. But I think we need to be cautious about what we take that to mean about the practical limitations of frontier LLMs.

• For simple models trained on simple, regular data, it's easy to specify what is and isn't in-distribution. For very complex models trained on a substantial fraction of human knowledge, it seems much less clear to me (I have yet to see a good approach to this problem; if anyone's aware of good research in this area I would love to know about it).
• There are many cases where humans are similarly bad at OOD generalization. The example above seems roughly isomorphic to the following problem: I give you half of the rules of an arbitrary game, and then ask you about the rest of the rules (eg: 'What happens if piece x and piece y come into contact?'. You can make some guesses based on the half of the rules you've seen, but there are likely to be plenty of cases where the correct generalization isn't clear. The case in the experiment seems less arbitrary to us, because we have extensive intuition built up about how the physical world operates (eg that objects fairly often change velocity but don't often change shape), but the model hasn't been shown info that would provide that intuition^[1]; why, then, should we expect it to generalize in a way that matches the physical world?
• We have a history of LLMs generalizing correctly in surprising ways that weren't predicted in advance (looking back at the GPT-3 paper is a useful reminder of how unexpected some of the emerging capabilities were at the time).

1. ^{^}

   Note that I haven't read the paper; I'm inferring this from the video summary they posted.

I am flattered to receive these Bayes points =) ; I would be crying tears of joy if there was a genuine slowdown but

• I generally think there are still huge gains to be made with scaling. Sometimes when people hear my criticism of scaling maximalism they patternmatch that to me saying scaling wont be as big as they think it is. To the contrary, I am saying scaling further will be as big as you think it will be, and additionally there is an enormous advance yet to come.

• How much evidence do we have of a genuine slowdown? Strawberry was about as big an advance as gpt3 tp gpt4 in my book. How credible are these twitter rumors?

o1-preview does not seem that much better than other models on extraordinarily difficult (and therefore OOD?) problems.

@Noosphere89 points in an earlier comment to a quote from the FrontierMath technical report: 

...we identified all problems that any model solved at least once—a total of four problems—and conducted repeated trials with five runs per model per problem (see Appendix B.2). We observed high variability across runs: only in one case did a model solve a question on all five runs (o1-preview for question 2). When re-evaluating these problems that were solved at least once, o1-preview demonstrated the strongest performance across repeated trials (see Section B.2).

I would love to see comparisons of o1 performance against other models in games like chess and Go. 

I would also find that interesting, although I don't think it would tell us as much about o1's general reasoning ability, since those are very much in-distribution.

Also, somewhat unrelated to this post, what do you and others think about x-risk in a world where explicit reasoners like o1 scale to AGI. To me this seems like one of the safest forms of AGI, since much of the computation is happening explicitly, and can be checked/audited by other AI systems and humans.

I agree that LLMs in general look like a simpler alignment problem than many of the others we could have been faced with, and having explicit reasoning steps seems to help further. I think monitoring still isn't entirely straightforward, since

1. CoT can be unfaithful to the actual reasoning process.
2. I suspect that advanced models will soon (or already?) understand that we're monitoring their CoT, which seems likely to affect CoT contents.
3. I would speculate that if such models were to become deceptively misaligned, they might very well be able to make CoT look innocuous while still using it to scheme.
4. At least in principle, putting optimization pressure on innocuous-looking CoT could result in models which were deceptive without deceptive intent.

But it does seem very plausibly helpful!

I think that the main conclusion is that large amounts of compute are still necessary in reasoning well OOD, and even though o1 is doing a little reasoning, it's a pretty small scale search (usually seconds or minutes of search.), which means that the fact that it's a derivative GPT-4o model matters a lot for it's incapacity, as it's pretty low on the compute scale compared to other models.