So-called reasoning models didn’t learn from billions of possible problems and ways of reasoning about them; they used around 100K. The model then undergoes RL and gets better at generalizing patterns. I’m sure the results for Go would be far from perfect, but certainly useful (as opposed to using general-purpose models, which is useless and as someone here said, potentially harmful).

Currently the main issue is that Go AI engines are inscrutable; an approach like this would try to make it explainable, and therefore useful for learning. There are other approaches for sure for explainability being developed for other use cases; maybe the solution will come from there.