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.
The paper explores the potential of large-scale models and supervised learning to achieve grandmaster-level chess performance without search algorithms.
Using action-values from Stockfish 16, the researchers trained a 270 million parameter transformer model to predict winning probabilities from chess positions.
The model achieved a Lichess blitz Elo of 2895, surpassing AlphaZero and GPT-3.5-turbo-instruct in some metrics without using domain-specific enhancements or search techniques.
The study shows the impact of model and dataset scale on chess AI performance, suggesting a future where complex reasoning could be captured by neural predictors.
The breakthroughs in AI over the past few years have been driven by the application of large-scale models and massive datasets. This paper investigates the application of such paradigms to the game of chess, traditionally dominated by engines using deep search algorithms and large databases of heuristics. Through the lens of supervised learning, the authors of the paper hypothesize that it is possible to achieve strong chess performance purely from learned action-values, without the traditional explicit search algorithms.
The team constructed a dataset by extracting and annotating 10 million games from Lichess with action-values sourced from the elite chess engine Stockfish 16. A transformer model with 270 million parameters was then trained to predict win probabilities for given chess board positions. The researchers systematically studied the effects of varying model sizes and dataset scales to understand their impact on generalization and chess performance.
The model's prowess is commendable, with a Lichess blitz Elo of 2895, which situates it in the grandmaster echelon. It also outperforms AlphaZero's policy and value networks—sans the Monte Carlo Tree Search—and GPT-3.5-turbo-instruct over a select set of metrics, solidifying the notion of neural predictors as strong generalizers. Key contributions include:
While achieving overwhelming success, the study acknowledges the limitations of lacking historical context which traditional engines use for strategic planning, exposing minor weaknesses. These were pragmatically mitigated through workarounds that, however, do not imply a search algorithm. Furthermore, the study clarifies the scale needed for similar neural predictors to bridge the gap between current model performance and oracle-powered engines like Stockfish 16.
The work underlines a shift in AI research, suggesting that highly complex algorithmic reasoning—once deemed exclusive to sophisticated search-based systems—can be distilled into transformer models using supervised learning. As such, transformers are not simply pattern recognition systems, but powerful approximators capable of substituting intricate algorithmic processes. The implications for future research are profound, promising further shifts in how AI is applied across varied cognitive domains.
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