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GPT 4.5 is a very tricky model to play chess against. It tricked me in the opening and was much better, then I managed to recover and reach a winning endgame. And then it tried to trick me again by suggesting illegal moves which would lead to it being winning again!
Given the Superalignment paper describes being trained on PGNs directly, and doesn't mention any kind of 'chat' reformatting or encoding metadata schemes, you could also try writing your games quite directly as PGNs. (And you could see if prompt programming works, since PGNs don't come with Elo metadata but are so small a lot of them should fit in the GPT-4.5 context window of ~100k: does conditioning on finished game with grandmaster-or-better players lead to better gameplay?)
1anaguma
I gave the model both the PGN and the FEN on every move with this in mind. Why do you think conditioning on high level games would help? I can see why for the base models, but I expect that the RLHFed models would try to play the moves which maximize their chances of winning, with or without such prompting.
but I expect that the RLHFed models would try to play the moves which maximize their chances of winning
RLHF doesn't maximize probability of winning, it maximizes a mix of token-level predictive loss (since that is usually added as a loss either directly or implicitly by the K-L) and rater approval, and god knows what else goes on these days in the 'post-training' phase muddying the waters further. Not at all the same thing. (Same way that a RLHF model might not optimize for correctness, and instead be sycophantic. "Yes master, it is just as you say!") I... (read more)