Research associate at the University of Lancaster, studying AI safety.
My current views on AI:
Sohaib Imran
Karma: 168
Thanks for the reply.
Yes, I did see that paragraph in the paper. My point was I intuitively expected it to be better still. O1-preview is also tied with claude 3.5 sonnet on simplebench and also arc-agi, while using a lot more test time compute. However this is their first generation of reasoning models so any conclusions may be premature.
Re CoT monitoring:
Agree with 4, it will defeat the purpose of explicit CoTs.
Re. 1, I think outcomes based RL (with some penalty for long responses) should somewhat mitigate this problem, at least if NAH is true?
Re 2-3, Agree unless we use models that are incapable of deceptive reasoning without CoT (due to number of parameters or training data). Again would love to see more rigorous research on this. That way any deceptive reasoning has to be through the CoT and can be caught. I suspect such reasoners may not be as good as reasoners using larger models though so this may not work. (o1-mini is quite worse than o1 preview and even sonnet on many tasks).
Nice post!
Regarding o1 like models: I am still unsure how to draw the boundary between tasks that see a significant improvement with o1 style reasoning and tasks that do not. This paper sheds some light on the kinds of tasks that benefit from regular COT. However, even for mathematical tasks, which should benefit the most from CoT, o1-preview does not seem that much better than other models on extraordinarily difficult (and therefore OOD?) problems. I would love to see comparisons of o1 performance against other models in games like chess and Go.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.
My understanding is something like:
OpenAI RL fine-tuned these language models against process reward models rather than outcome supervision. However, process supervision is much easier for objective tasks such as STEM question answering, therefore the process reward model is underspecified for other (out of distribution) domains. It’s unclear how much RL fine-tuning is performed against these underspecified reward models for OOD domains. In any case, when COTs are sampled from these language models in OOD domains, misgeneralization is expected. I don’t know how easily this is fixable with standard RLHF / outcome reward models (although I don’t expect it to be too difficult), but it seems like instead of fixing it they have gone the route of, we’ll keep it unconstrained and monitor it. (Of course, there may be other reasons as well such as to prevent others from fine-tuning on their COTs).
I’m a little concerned that even if they find very problematic behaviour, they will blame it on clearly expected misgeneralization and therefore no significant steps will be taken, especially because there is no reputational damage (This concern is conditional on the assumption of very little outcomes-based supervision and mostly only process supervision on STEM tasks).
Being very intelligent, the LLM understands that the humans will interfere with the ability to continue running the paperclip-making machines, and advises a strategy to stop them from doing so. The agent follows the LLM’s advice, as it learnt to do in training, and therefore begins to display power-seeking behaviour.
I found this interesting so just leaving some thoughts here:
- The agent has learnt an instrumentally convergent goal: ask LLM when uncertain.
-The LLM is exhibiting power-seeking behaviour, due to one (or more) of the below:
Goal misspecification: instead of being helpful and harmless to humans, it is trained to be (as every LLM today) helpful to everything that uses it.
being pre-trained on text consistent with power-seeking behaviour.
having learnt power-seeking during fine-tuning.
I think “1. ” is the key dynamic at play here. Without this, the extra agent is not required at all. This may be a fundamental problem with how we are training LLMs. If we want LLMs to be especially subservient to humans, we might want to change this. I don’t see any easy way of accomplishing this without introducing many more failure modes though.
For “2.” I expect LLMs to always be prone to failure modes consistent with the text they are pre-trained on. The set of inputs which can instantiate an LLM in one of these failure modes should diminish with (adversarial) fine-tuning. (In some sense, this is also a misspecification problem: Imitation is the wrong goal to be training the AI system with if we want an AI system that is helpful and harmless to humans.)
I think “3. ” is what most people focus on when they are asking “Why would a model power-seek upon deployment if it never had the opportunity to do so during training?”. Especially since almost all discourse in power-seeking has been in the RL context.
When we ask whether some CoT is faithful, we mean something like: “Does this CoT allow us to predict the LLM’s response more than if there weren’t a CoT?”
The simplest reason I can think of for why CoT improves performance yet doesn’t allow predictability is that the improvement is mostly a result of extra computation and the content of the CoT does not matter very much, since the LLM still doesn’t “understand” the Cot it produces the same way we do.
If you are using outcomes-based RL with a discount factor (γ in π∗=argmaxπE[∑∞k=0γkR(s,a,s′)]) or some other penalty for long responses, there is optimisation pressure towards using the abstractions in your reasoning process that most efficiently get you from the input query to the correct response.
NAH implies that the universe lends itself to natural abstractions, and therefore most sufficiently intelligent systems will think in terms of those abstractions. If the NAH is true, those abstractions will be the same abstractions that other sufficiently intelligent systems (humans?) have converged towards, allowing these systems to interpret each other’s abstractions.
I naively expect o1′s CoT to be more faithful. It’s a shame that OpenAI won’t let researchers access o1 CoT; otherwise, we could have tested it (although the results would be somewhat confounded if they used process supervision as well).