Something weird is happening with LLMs and chess

But there's really nothing about chess that makes reasoning a prerequisite, a win is a win as long as it's a win. This is kind of a semantics game: it's a question of whether the degree of skill people observe in an LLM playing chess is actually some different quantity than the chance it wins.

I mean at some level you're saying that no matter how close to 1 the win probability (1 - epsilon) gets, both of the following are true:

A. you should always expect for the computation that you're able to do via conscious reasoning alone to always be sufficient, at least in principle, to asymptotically get a higher win probability than a model, no matter what the model's win probability was to begin with

B. no matter how close to 1 that the model's win rate p=(1 - epsilon) gets, because logical inference is so non-smooth, the win rate on yet-unseen data is fundamentally algorithmically random/totally uncorrelated to in-distribution performance, so it's never appropriate to say that a model can understand or to reason

To me it seems that people are subject to both of these criteria, though. They have a tendency to cap out at their eventual skill cap unless given a challenge to nudge them to a higher level, and likewise possession of logical reasoning doesn't let us say much at all about situations that their reasoning is unfamiliar with.

I also think, if you want to say that what LLMs do has nothing to do with understanding or ability, then you also have to have an alternate explanation for the phenomenon of AlphaGo defeating Lee Sedol being a catalyst for top Go players being able to rapidly increase their own rankings shortly after.