New Self-Supervised Learning Method Learns Chess Representations Without Reinforcement Learning
Researchers have introduced RePAIR, a self-supervised representation learning architecture that encodes sequential chess positions into compact, semantically meaningful representations. The system combines three established deep learning approaches — Masked Autoencoders, Joint Embedding Predictive Architectures, and BERT — to reconstruct masked board states and reason about piece movements. The work demonstrates that meaningful chess concepts can emerge from self-supervised learning alone, without the computational cost of reinforcement learning.
RePAIR (Representation Prediction via Autoencoding using Iterative Refinement) is a novel architecture presented by Christoph Koller and accepted for oral presentation at the IEEE Conference on Games 2026. The system masks large portions of a sequence of latent chess board states, then uses a lightweight Predictor module to repair those gaps in a lower-dimensional embedding space, drawing on principles from BERT, MAE, and JEPA. Experiments show that the Encoder learns to cluster meaningful chess concepts in the resulting latent space without any explicit supervision or reinforcement learning signals. The model is also capable of reconstructing masked board states in a way that reflects plausible piece movements, suggesting genuine positional reasoning. Additionally, the learned representation space enables intuitive analysis of chess games by visualizing game trajectories through the semantically structured latent space, offering a potential tool for game dissection and understanding.
What's missing
The paper does not report quantitative benchmarks comparing RePAIR's positional reasoning or concept clustering against existing chess AI systems (e.g., Stockfish, AlphaZero, or Leela Chess Zero), making it difficult to assess the practical strength of the learned representations. The scale of training data, computational requirements, and whether the approach generalizes beyond chess to other sequential domains are not detailed in the abstract.
What different sources said
- arXiv cs.LGCenter
RePAIR: Predictive Self-Supervised Representation Learning in Chess
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