[fenomas]

As I understood that part, in RL for LLMs you take questions for which the model already sometimes emits correct answers, and then repeatedly infer while reinforcing the activations the model made during correct responses, which lets it evolve its own ways of more reliably reaching the right answer.

(Hence the analogy to training AlphaGo, wherein you take a model that sometimes wins games, and then play a bunch of games while reinforcing the cases where it won, so that it evolves its own ways of winning more often.)

[quantumspandex]

AlphaGo seems more like an automated process to me because you can start from nothing except the algorithm and the rules. Since a Go game only has 2 outcomes most of the time, and the model can play with itself, it is guaranteed to learn something during self-play.

In the LLM case you have to have an already capable model to do RL. Also I feel like the problem selection part is important to make sure it's not too hard. So there's still much labor involved.

[fenomas]

Yes, IIUC those points are correct - you need relatively capable models, and well-crafted questions. The comparison with AlphaGo is that the processes are analogous, not identical - the key point being that in both cases the model is choosing its own path towards a goal, not just imitating the path that a human labeler took.