Grandmaster-level chess without search algorithms

This 2024 work demonstrates that a large language model (transformer) trained purely on chess positions and human games can reach grandmaster level without any explicit search tree — a result that surprised the chess AI community, which had long assumed that MCTS was necessary for chess at that level. The system achieves 2895 Elo (grandmaster level) with a 270M parameter transformer, by training on 10 million games filtered to only high-Elo games. The work inverts the standard assumption: rather than using neural networks to guide search (as in AlphaZero), the neural network alone can implicitly perform the reasoning that search makes explicit. For the broader AI research agenda, the result suggests that large-scale pattern recognition over high-quality data can substitute for algorithmic search in more domains than previously thought — and that the quality and filtering of training data may be more important than architectural choices. Hassabis uses this result to argue for a 'data curriculum' approach to training general AI: exposing models to problems at the frontier of human difficulty, not just average human performance.