New Self-Supervised Learning Method Learns Chess Representations Without Reinforcement Learning
Researchers introduced RePAIR, a self-supervised learning architecture that combines techniques from masked autoencoders, joint embedding predictive architectures, and BERT to learn meaningful chess board representations. The method masks portions of sequential chess positions and uses a lightweight predictor to reconstruct them in a lower-dimensional space, allowing the model to reason about piece movements. This approach could enable faster analysis of chess games and demonstrates that complex game understanding can emerge without expensive reinforcement learning methods.
A new preprint on arXiv describes RePAIR (Representation Prediction via Autoencoding using Iterative Refinement), a self-supervised learning architecture designed to encode sequential data like consecutive chess positions into compact, meaningful representations. The method synthesizes three existing techniques: Masked Autoencoders (MAE), Joint Embedding Predictive Architectures (JEPA), and BERT. The core approach masks large portions of latent state sequences and applies a lightweight predictor to reconstruct the gaps in a lower-dimensional embedding space. Experiments demonstrate that the encoder refines board representations such that meaningful chess concepts emerge as clusters in the latent space, and the model can reason about piece movements without relying on computationally expensive reinforcement learning. The resulting representation space enables intuitive analysis of chess games by observing game path trajectories in the learned semantic space.
What's missing
The paper does not provide quantitative performance comparisons against baseline methods or other self-supervised approaches, nor does it specify the size of the chess dataset used for training or provide details on computational requirements and training time.
What different sources said
- arXiv cs.LGCenter
RePAIR: Predictive Self-Supervised Representation Learning in Chess
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