[adroniser]

Hmm well the reason a pre-trained transformer is a fancy sentence completion engine is because that is what it is trained on, cross entropy loss on next token prediction. As I say, if you train an LLM to do math proofs, it learns to solve 4 out of the 6 IMO problems. I feel like you're not appreciating how impressive that is. And that is only possible because of the RL aspect of the system.

To be clear, i'm not claiming that you take an LLM and do some RL on it and suddenly it can do particular tasks. I'm saying that if you train it from scratch using RL it will be able to do certain well defined formal tasks.

Idk what you mean about the online learning ability tbh. The paper uses it in the exact way you specify, which is that it uses RL to play montezuma's revenge and gets better on the fly.

Similar to my point about the inference time RL ability of the alphaProof LLM. That's why I emphasized that RL is done at inference time, like each proof you do it uses to make itself better for next time.

I think you are taking LLM to mean GPT style models, and I am taking LLM to mean transformers which output text, and they can be trained to do any variety of things.

[HarHarVeryFunny]

A transformer, regardless of what it is trained to do, is just a pass thru architecture consisting of a fixed number of layers, no feedback paths, and no memory from one input to the next. Most of it's limitations (wrt AGI) stem from the architecture. How you train it, and on what, can't change that.

Narrow skills like playing Chess (DeepBlue), Go, or math proofs are impressive in some sense, but not the same as generality and/or intelligence which are the hallmarks of AGI. Note that AlphaProof, as the same suggests, has more in common with AlphaGo and AlphaFold than a plain transformer. It's a hybrid neuro-symbolic approach where the real power is coming from the search/verification component. Sure, RL can do some impressive things when the right problem presents itself, but it's not a silver bullet to all machine learning problems, and few outside of David Silver think it's going to be the/a way to achieve AGI.

[adroniser]

I agree with you that transformers are probably not the architecture of choice. Not sure what that has to do with the viability of RL though.