No, I’m saying that “adding ‘logic’ to AIs” doesn’t (currently) look like “figure out how to integrate insights from expert systems/explicit bayesian inference into deep learning”, it looks like “use deep learning to nudge the AI toward being better at explicit reasoning by making small changes to the training setup”. The standard “deep learning needs to include more logic” take generally assumes that you need to add the logic/GOFAI juice in explicitly, while in practice people do a slightly different RL or supervised finetuning setup instead.
(EDITED to add: so while I do agree that “LMs are bad at the things humans do with ‘logic’ and good at ‘intuition’ is a decent heuristic, I think the distinction that we’re talking about here is instead about the transparency of thought processes/”how the thing works” and not about if the thing itself is doing explicit or implicit reasoning. Do note that this is a nitpick (as the section header says) that’s mainly about framing and not about the core content of the post.)
That being said, I’ll still respond to your other point:
Chain of thought is a wonderful thing, it clears a space where the model will just earnestly confess its inner thoughts and plans in a way that isn’t subject to training pressure, and so it, in most ways, can’t learn to be deceptive about it.
I agree that models with CoT (in faithful, human-understandable English) are more interpretable than models that do all their reasoning internally. And obviously I can’t really argue against CoT being helpful in practice; it’s one of the clear baselines for eliciting capabilities.
But I suspect you’re making a distinction about “CoT” that is actually mainly about supervised finetuning vs RL, and not a benefit about CoT in particular. If the CoT comes from pretraining or supervised fine-tuning, the ~myopic next-token-prediction objective indeed does not apply much if training pressure in the relevant ways.[1] Once you start doing any outcome-based supervision (i.e. RL) without good regularization, I think the story for CoT looks less clear. And the techniques people use for improving CoT tend to involve upweighting entire trajectories based on their reward (RLHF/RLAIF with your favorite RL algorithm) which do incentivize playing the training game unless you’re very careful with your fine-tuning.
(EDITED to add: Or maybe the claim is, if you do CoT on a ‘secret’ scratchpad (i.e. one that you never look at when evaluating or training the model), then this would by default produce more interpretable thought processes?)
I’m not sure this is true in the limit (e.g. it seems plausible to me that the Solomonoff prior is malign). But it’s most likely true in the next few years and plausibly true in all practical cases that we might consider.
No, I’m saying that “adding ‘logic’ to AIs” doesn’t (currently) look like “figure out how to integrate insights from expert systems/explicit bayesian inference into deep learning”, it looks like “use deep learning to nudge the AI toward being better at explicit reasoning by making small changes to the training setup”. The standard “deep learning needs to include more logic” take generally assumes that you need to add the logic/GOFAI juice in explicitly, while in practice people do a slightly different RL or supervised finetuning setup instead.
(EDITED to add: so while I do agree that “LMs are bad at the things humans do with ‘logic’ and good at ‘intuition’ is a decent heuristic, I think the distinction that we’re talking about here is instead about the transparency of thought processes/”how the thing works” and not about if the thing itself is doing explicit or implicit reasoning. Do note that this is a nitpick (as the section header says) that’s mainly about framing and not about the core content of the post.)
That being said, I’ll still respond to your other point:
I agree that models with CoT (in faithful, human-understandable English) are more interpretable than models that do all their reasoning internally. And obviously I can’t really argue against CoT being helpful in practice; it’s one of the clear baselines for eliciting capabilities.
But I suspect you’re making a distinction about “CoT” that is actually mainly about supervised finetuning vs RL, and not a benefit about CoT in particular. If the CoT comes from pretraining or supervised fine-tuning, the ~myopic next-token-prediction objective indeed does not apply much if training pressure in the relevant ways.[1] Once you start doing any outcome-based supervision (i.e. RL) without good regularization, I think the story for CoT looks less clear. And the techniques people use for improving CoT tend to involve upweighting entire trajectories based on their reward (RLHF/RLAIF with your favorite RL algorithm) which do incentivize playing the training game unless you’re very careful with your fine-tuning.
(EDITED to add: Or maybe the claim is, if you do CoT on a ‘secret’ scratchpad (i.e. one that you never look at when evaluating or training the model), then this would by default produce more interpretable thought processes?)
I’m not sure this is true in the limit (e.g. it seems plausible to me that the Solomonoff prior is malign). But it’s most likely true in the next few years and plausibly true in all practical cases that we might consider.