[2603.29761] Tracking vs. Deciding: The Dual-Capability Bottleneck in Searchless Chess Transformers

Computer Science > Artificial Intelligence arXiv:2603.29761 (cs) [Submitted on 31 Mar 2026] Title:Tracking vs. Deciding: The Dual-Capability Bottleneck in Searchless Chess Transformers Authors:Quanhao Li, Wei Jiang View a PDF of the paper titled Tracking vs. Deciding: The Dual-Capability Bottleneck in Searchless Chess Transformers, by Quanhao Li and Wei Jiang View PDF HTML (experimental) Abstract:A human-like chess engine should mimic the style, errors, and consistency of a strong human player rather than maximize playing strength. We show that training from move sequences alone forces a model to learn two capabilities: state tracking, which reconstructs the board from move history, and decision quality, which selects good moves from that reconstructed state. These impose contradictory data requirements: low-rated games provide the diversity needed for tracking, while high-rated games provide the quality signal for decision learning. Removing low-rated data degrades performance. We formalize this tension as a dual-capability bottleneck, P <= min(T,Q), where overall performance is limited by the weaker capability. Guided by this view, we scale the model from 28M to 120M parameters to improve tracking, then introduce Elo-weighted training to improve decisions while preserving diversity. A 2 x 2 factorial ablation shows that scaling improves tracking, weighting improves decisions, and their combination is superadditive. Linear weighting works best, while overly aggressive wei...