Scaling Mathematical Proof Supervision from Structured Textbooks

Youqing Fang, Zhouqi Hua, Yinhao Tang, Yuzhe Gu, Lingkai Kong, Bin Liu, Weiming Zhang, Kai Chen, Yanan Sun, Wenwei Zhang

Scaling Mathematical Proof Supervision from Structured Textbooks: 39 upvotes on Hugging Face Daily Papers, #1 of 21 papers on 2026-11-12. Day-by-day upvote history.

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It had 39 upvotes by its first day, 39 upvotes by the next day, 39 upvotes by day 3, 39 upvotes by day 7, and has 39 now. It ranked #15 of 133 papers in its week and #45 of 340 in its month.

Mathematical supervision for language models is typically flattened into isolated problem-solution pairs, discarding the curricular organization and evidential structure of the original sources. We introduce TOME-Math (Textbook-Oriented Mathematical Evidence), a large-scale corpus that converts 2,118 textbooks spanning the core areas of university-level mathematics subjects into approximately 380K structured proof-supervision records while preserving chapter order, proposition-proof links, dependency references, and source provenance. Leveraging these structural signals, we explore curriculum learning across multiple mathematical subjects and characterize scaling patterns with supervision volume and source breadth. At full scale, fine-tuning on TOME-Math raises the unweighted mean score across our held-out evaluations and established public benchmarks by 16.7 percentage points over the base model, while exceeding all three public instruction-data baselines evaluated in aggregate. Our findings highlight structured textbooks as both a scalable source of mathematical supervision and an empirical framework for studying the impact of data organization on mathematical reasoning. We will release the corpus to facilitate further research on structured mathematical supervision.

Paper page on Hugging Face · arXiv

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