Grandmaster-Level Chess Without Search
The recent breakthrough successes in machine learning are mainly attributed
to scale: namely large-scale attention-based architectures and datasets of
unprecedented scale. This paper investigates the impact of training at scale
for chess. Unlike traditional chess engines that rely on complex heuristics,
explicit search, or a combination of both, we train a 270M parameter
transformer model with supervised learning on a dataset of 10 million chess
games. We annotate each board in the dataset with action-values provided by the
powerful Stockfish 16 engine, leading to roughly 15 billion data points. Our
largest model reaches a Lichess blitz Elo of 2895 against humans, and
successfully solves a series of challenging chess puzzles, without any
domain-specific tweaks or explicit search algorithms. We also show that our
model outperforms AlphaZero's policy and value networks (without MCTS) and
GPT-3.5-turbo-instruct. A systematic investigation of model and dataset size
shows that strong chess performance only arises at sufficient scale. To
validate our results, we perform an extensive series of ablations of design
choices and hyperparameters.
65c4687bc3e219990818da3c
Anian Ruoss
claimed_verified
2024-02-09T08:05:26.326Z
65c51758522ee3a4edd917d0
/avatars/2ea3b64b5eeb34e2bfe821e4210f1b3e.svg
Anian Ruoss
user
anianruoss
65c4687bc3e219990818da3d
Grégoire Delétang
65c4687bc3e219990818da3e
Sourabh Medapati
admin_assigned
2024-02-08T09:51:24.772Z
648888b165e25cc87b6ab206
/avatars/c972e4ff17a446c4d2f803fe9315ce12.svg
Sourabh Medapati
user
smedapati
65c4687bc3e219990818da3f
Jordi Grau-Moya
65c4687bc3e219990818da40
Li Kevin Wenliang
65c4687bc3e219990818da41
Elliot Catt
65c4687bc3e219990818da42
John Reid
65c4687bc3e219990818da43
Tim Genewein
Grandmaster-Level Chess Without Search
The recent breakthrough successes in machine learning are mainly attributed
to scale: namely large-scale attention-based architectures and datasets of
unprecedented scale. This paper investigates the impact of training at scale
for chess. Unlike traditional chess engines that rely on complex heuristics,
explicit search, or a combination of both, we train a 270M parameter
transformer model with supervised learning on a dataset of 10 million chess
games. We annotate each board in the dataset with action-values provided by the
powerful Stockfish 16 engine, leading to roughly 15 billion data points. Our
largest model reaches a Lichess blitz Elo of 2895 against humans, and
successfully solves a series of challenging chess puzzles, without any
domain-specific tweaks or explicit search algorithms. We also show that our
model outperforms AlphaZero's policy and value networks (without MCTS) and
GPT-3.5-turbo-instruct. A systematic investigation of model and dataset size
shows that strong chess performance only arises at sufficient scale. To
validate our results, we perform an extensive series of ablations of design
choices and hyperparameters.
A large-scale transformer model trained on a vast dataset of chess games outperforms traditional chess engines and other state-of-the-art models without domain-specific tweaks.
transformer model supervised learning action-values Lichess blitz Elo chess puzzles AlphaZero MCTS GPT-3.5-turbo-instruct model size dataset size