Grandmaster-Level Chess Without Search
Anian Ruoss Grégoire Delétang Sourabh Medapati Jordi Grau-Moya Li Kevin Wenliang Elliot Catt John Reid Tim Genewein
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.