Pretraining Transformers for Chess Puzzle Difficulty Prediction

This paper presents our third-place solution for the FedCSIS 2025 Challenge: Predicting Chess Puzzle Difficulty -Second Edition.Building on our prior GlickFormer architecture, we develop a transformer-based approach featuring a novel multitask pretraining strategy that combines masked-square reconstruction with solution policy prediction.Our spatial-only architecture directly embeds solution moves, eliminating temporal modules, while integrating human-centric priors through Maia-2 engine solve-rate predictions.Evaluated on the Lichess puzzle corpus, our approach reduces validation MSE by 30.4% compared to from-scratch training and achieves competitive results (test MSE: 55.9k) despite distribution shifts in the competition environment.In an auxiliary uncertainty-masking side competition organized post hoc, our simple calibration-sensitivity mask achieved the best score and won the side competition.Details are provided in the Appendix.