Something weird is happening with LLMs and chess

I feel like the article neglects one obvious possibility: that OpenAI decided that chess was a benchmark worth "winning", special-cases chess within gpt-3.5-turbo-instruct, and then neglected to add that special-case to follow-up models since it wasn't generating sustained press coverage.

This seems quite likely to me, but did they special case it by reinforcement training it into the LLM (which would be extremely interesting in how they did it and what its internal representation looks like) or is it just that when you make an API call to OpenAI, the machine on the other end is not just a zillion-parameter LLM but also runs an instance of Stockfish?

That's easy to test, invent a new chess variant and see how the model does.

You're imagining LLMs don't just regurgitate and recombine things they already know from things they have seen before. A new variant would not be in the dataset so would not be understood. In fact this is quite a good way to show LLMs are NOT thinking or understanding anything in the way we understand it.

Yes, that's how you can really tell if the model is doing real thinking and not recombinating things. If it can correctly play a novel game, then it's doing more than that.

I wonder what the minimal amount of change qualifies as novel?

"Chess but white and black swap their knights" for example?

I wonder what would happen with a game that is mostly chess (or chess with truly minimal variations) but with all the names changed (pieces, moves, "check", etc, all changed). The algebraic notation is also replaced with something else so it cannot be pattern matched against the training data. Then you list the rules (which are mostly the same as chess).

None of these changes are explained to the LLM, so if it can tell it's still chess, it must deduce this on its own.

Would any LLM be able to play at a decent level?