TOME-Math：基于结构化教科书的数学证明监督语料库
Scaling Mathematical Proof Supervision from Structured Textbooks
TOME-Math 将 2,118 本大学数学教科书转化为约 380K 结构化证明监督记录，保留章节顺序、命题-证明链接和依赖参考，用于微调后使语言模型数学推理能力提升 16.7 个百分点，超越所有三个公共指令数据基线。该语料库将发布以促进结构化数学监督研究。
Abstract
A large-scale corpus of structured proof-supervision records derived from university mathematics textbooks improves language model mathematical reasoning by 16.7 percentage points over a base model when used for fine-tuning.
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.
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