用54万道数学题训练出顶尖推理模型，关键在代码执行与解法筛选。
AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset
- 构建54万道高质量数学题数据集，含320万条长链推理答案。
- 通过迭代训练+代码执行，生成170万条高质工具融合推理解法。
- 提出生成式解法选择机制，比多数投票更优，适合数学推理研究者。
本文介绍我们在人工智能数学奥赛-进展奖2（AIMO-2）竞赛中的获胜方案。我们的方法基于三大支柱：首先，创建包含54万道独特高质量数学题（含奥数级题目）及其320万条长链推理解答的大型数据集；其次，开发一种将代码执行与长推理模型结合的新方法，通过迭代训练、生成与质量过滤，获得170万条高质量工具集成推理解答；第三，设计一个解法筛选管道，使模型能从多个候选解中选出最优解。我们证明，这种生成式解法选择（GenSelect）显著优于多数投票基线。综合上述技术，训练出一系列在数学推理基准上达到领先水平的模型。为促进后续研究，我们以商业友好许可发布全部代码、模型及完整OpenMathReasoning数据集。
原文摘要 · Abstract (English)
This paper presents our winning submission to the AI Mathematical Olympiad - Progress Prize 2 (AIMO-2) competition. Our recipe for building state-of-the-art mathematical reasoning models relies on three key pillars. First, we create a large-scale dataset comprising 540K unique high-quality math problems, including olympiad-level problems, and their 3.2M long-reasoning solutions. Second, we develop a novel method to integrate code execution with long reasoning models through iterative training, generation, and quality filtering, resulting in 1.7M high-quality Tool-Integrated Reasoning solutions. Third, we create a pipeline to train models to select the most promising solution from many candidates. We show that such generative solution selection (GenSelect) can significantly improve upon majority voting baseline. Combining these ideas, we train a series of models that achieve state-of-the-art results on mathematical reasoning benchmarks. To facilitate further research, we release our code, models, and the complete OpenMathReasoning dataset under a commercially permissive license.
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