Key Points

- The paper investigates the impact of training at scale for chess without relying on traditional chess engines that use complex heuristics, explicit search, or a combination of both.

- The study trains a 270M parameter transformer model using supervised learning on a dataset of 10 million chess games, annotated with action-values provided by the powerful Stockfish 16 engine.

- The largest model reaches a Lichess blitz Elo of 2895 against humans and successfully solves challenging chess puzzles without domain-specific tweaks or explicit search algorithms.

- The model outperforms AlphaZero’s policy and value networks and GPT-3.5-turbo-instruct.

- The research demonstrates that supervised learning at scale can lead to strong chess play and that strong chess capabilities emerge only at sufficient dataset and model scale.

- The study provides insights into the dataset creation, predictors and policies, and the evaluation metrics used to compare the models' performance.

- A series of experimental variants and ablations are tested, including predictor targets, network depth, data sampler, value binning, loss function, and Stockfish time limit.

- The paper acknowledges limitations, including the inability to completely close the gap to Stockfish 16 and the necessity of making a serious attempt to produce a strong chess policy and calibrating its playing strength.

- The study concludes that it is possible to distill an approximation of Stockfish 16 into a feed-forward transformer through standard supervised training, which adds to the growing body of literature showing that complex algorithms can be distilled into feed-forward transformers.

Summary

Research Findings
The research paper explores the impact of training a 270M parameter transformer model with supervised learning on a dataset of 10 million chess games. The paper demonstrates that the trained model achieves a Lichess blitz Elo of 2895 against humans and solves challenging chess puzzles without domain-specific tweaks or explicit search algorithms. The model outperforms AlphaZero’s policy and value networks as well as GPT-3.5-turbo-instruct. The study investigates the influence of model and dataset size on strong chess performance, with findings indicating that performance improves with larger scale.

Validation and Limitations
To validate the results, the paper conducts ablations of design choices and hyperparameters, showing that the model’s performance improves with sufficient scale. Additionally, the paper discusses the limitations of the approach, such as the model's inability to completely close the performance gap with Stockfish 16 and its blindness to threefold repetition, which impacts the model's play. The authors also compare the model's performance to traditional chess engines and other models, and investigate the challenges and limitations of the approach, including the need to access game history and the occurence of threefold repetition.

Overall, the paper concludes that it is possible to distill an approximation of Stockfish 16 into a feed-forward transformer via standard supervised training and suggests a paradigm shift in the perception of large transformers as powerful techniques for general algorithm approximation. The study marks a significant advancement in the use of large-scale supervised learning techniques for training complex model architectures to excel in specialized domains like chess.

Reference: https://arxiv.org/abs/2402.04494