Papers › Amortized Planning with Large-Scale Transformers: A Case Study on Chess

Amortized Planning with Large-Scale Transformers: A Case Study on Chess

7 Feb 2024arXiv:2402.04494archive 2025-07-28

Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya, Li Kevin Wenliang, Elliot Catt, John Reid, Cannada A. Lewis, Joel Veness, Tim Genewein

This paper uses chess, a landmark planning problem in AI, to assess transformers' performance on a planning task where memorization is futile x2013 even at a large scale. To this end, we release ChessBench, a large-scale benchmark dataset of 10 million chess games with legal move and value annotations (15 billion data points) provided by Stockfish 16, the state-of-the-art chess engine. We train transformers with up to 270 million parameters on ChessBench via supervised learning and perform extensive ablations to assess the impact of dataset size, model size, architecture type, and different prediction targets (state-values, action-values, and behavioral cloning). Our largest models learn to predict action-values for novel boards quite accurately, implying highly non-trivial generalization. Despite performing no explicit search, our resulting chess policy solves challenging chess puzzles and achieves a surprisingly strong Lichess blitz Elo of 2895 against humans (grandmaster level). We also compare to Leela Chess Zero and AlphaZero (trained without supervision via self-play) with and without search. We show that, although a remarkably good approximation of Stockfish's search-based algorithm can be distilled into large-scale transformers via supervised learning, perfect distillation is still beyond reach, thus making ChessBench well-suited for future research.

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centipawns_to_win_probability google-deepmind/searchless_chess/src/utils.py official repository ran fingerprinted Apache-2.0 (permissive) · 591fdb4ac81fd268 · report
compute_return_buckets_from_returns google-deepmind/searchless_chess/src/utils.py official repository ran Apache-2.0 (permissive) · dcd92d478a8bcc64 · report
get_uniform_buckets_edges_values google-deepmind/searchless_chess/src/utils.py official repository ran fingerprinted Apache-2.0 (permissive) · 20d0be0806826b79 · report
sinusoid_position_encoding google-deepmind/searchless_chess/src/transformer.py official repository ran Apache-2.0 (permissive) · f915c59353ea23e2 · report
tokenize google-deepmind/searchless_chess/src/tokenizer.py official repository ran Apache-2.0 (permissive) · 441d1203a85b9ca6 · report
embed_sequences google-deepmind/searchless_chess/src/transformer.py official repository unverified Apache-2.0 (permissive) · fa2f70d86d6bc6e4 · report
layer_norm google-deepmind/searchless_chess/src/transformer.py official repository unverified Apache-2.0 (permissive) · a145acc7af542d06 · report

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Memorization

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AdamAlphaZeroAttentionAttention DropoutBPECosine AnnealingDense ConnectionsDropoutGPT-3Layer NormalizationLinear LayerLinear Warmup With Cosine AnnealingMulti-Head AttentionResidual ConnectionSoftmaxWeight Decay

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