Grandmaster-Level Chess Without Search
DeepMind shows that a 270M-parameter transformer trained purely with supervised learning on Stockfish-generated data reaches grandmaster-level chess without any search at inference time.
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ChessBench dataset: Training set of 10M games and 15B data points, each annotated with Stockfish 16 action-values to distill a strong search-based engine into a feed-forward policy.
Grandmaster Elo: Achieves Lichess blitz Elo of 2895 against humans, solidly grandmaster-class and beating prior neural chess systems that didn't use explicit search.
Puzzle solving: Solves a series of challenging chess puzzles that require deep tactical awareness - a stronger test of pattern recognition than standard game play.
Scale over search: Positions transformer-based chess as a scale story rather than a domain-engineering story; no MCTS, alpha-beta, or handcrafted heuristics are used at inference.