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u/AnnualDesperate1599

▲ 1 r/ComputerChess

https://preview.redd.it/mk77h19qq4zg1.png?width=1490&format=png&auto=webp&s=a375a1f540f7c152aba74549a3c6923a16459aea

I updated the GPT structured chess bot by adding a bit of calculation. I imitated MCTS searching process by letting previous model (which, frequently make mistakes) be the policy head (provides top 8 moves and probabilities) , and use stockfish (strictly limiting its depth) as value head, return (N, Q).

It is like dividing one Alpha0 model into 2 parts.

Model reduces 80% of blunders, while previous model is still dominating the search. After this, I will change the stockfish into handcraft evaluation, or thinking of training an individual network.

Updated model has been put on lichess: https://lichess.org/@/CatieChess-Magnus

and will soon be put on main web catiechess.com

reddit.com
u/AnnualDesperate1599 — 11 days ago
▲ 2 r/chessprogramming

https://preview.redd.it/mk77h19qq4zg1.png?width=1490&format=png&auto=webp&s=a375a1f540f7c152aba74549a3c6923a16459aea

I updated the GPT structured chess bot by adding a bit of calculation. I imitated MCTS searching process by letting previous model (which, frequently make mistakes) be the policy head (provides top 8 moves and probabilities) , and use stockfish (strictly limiting its depth) as value head, return (N, Q).

It is like dividing one Alpha0 model into 2 parts.

Model reduces 80% of blunders, while previous model is still dominating the search. After this, I will change the stockfish into handcraft evaluation, or thinking of training an individual network.

Updated model has been put on lichess: https://lichess.org/@/CatieChess-Magnus

and will soon be put on main web catiechess.com

reddit.com
u/AnnualDesperate1599 — 11 days ago
▲ 20 r/ComputerChess+1 crossposts

I’m the author of a small research project on sequence-based chess models.

The model is not a traditional search engine. It does not run engine-style tree search over future positions. Instead, it treats a chess game as a sequence of moves and predicts the next move autoregressively, similar to how language models predict the next token.

The part I’m trying to evaluate more rigorously is whether the model is using full game history in a meaningful way, or mostly imitating local move patterns.

Challenge it on https://lichess.org/@/CatieChess-Magnus

u/AnnualDesperate1599 — 12 days ago