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A native C++ and distributed self-play learning system reaching superhuman calibrated chess strength in 2.5 days on eight consumer GPUs, with a live application and arXiv paper.
GamesPython · C++GitHub @BertilBraunOnlineOwner unconfirmed
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AlphaZero-style chess trained from self-play on multi GPU nodes: native C++ search with batched TensorRT inference, Python training. 3,251 Elo at 100k searches against fixed-node Stockfish 13 (paper, arXiv:2609.37447); 3,338 with a follow-up attention network.
Taken from the repository README
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How strong can an AlphaZero-style chess system become under a very limited compute budget when the entire learning
loop is engineered for efficiency? This project trained chess models from random initialization through self-play,
without human games or pretrained chess weights, while sharing eight RTX 4070 SUPER GPUs among self-play, learning,
and evaluation.Read the full README ↗The full README is unavailable right now. Read it at the original link. The saved excerpt is shown below.
How strong can an AlphaZero-style chess system become under a very limited compute budget when the entire learning
loop is engineered for efficiency? This project trained chess models from random initialization through self-play,
without human games or pretrained chess weights, while sharing eight RTX 4070 SUPER GPUs among self-play, learning,
and evaluation.Read the full README ↗