title: DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and
    Verifiable Mathematical Dataset for Advancing Reasoning
summary: The capacity for complex mathematical reasoning is a key benchmark for
  artificial intelligence. While reinforcement learning (RL) applied to LLMs
  shows promise, progress is significantly hindered by the lack of large-scale
  training data that is sufficiently challenging, possesses verifiable answer
  formats suitable for RL, and is free from contamination with evaluation
  benchmarks. To address these limitations, we introduce DeepMath-103K, a new,
  large-scale dataset comprising approximately 103K mathematical problems,
  specifically designed to train advanced reasoning models via RL. DeepMath-103K
  is curated through a rigorous pipeline involving source analysis, stringent
  decontamination against numerous benchmarks, and filtering for high difficulty
  (primarily Levels 5-9), significantly exceeding existing open resources in
  challenge. Each problem includes a verifiable final answer, enabling rule-based
  RL, and three distinct R1-generated solutions suitable for diverse training
  paradigms like supervised fine-tuning or distillation. Spanning a wide range of
  mathematical topics, DeepMath-103K promotes the development of generalizable
  reasoning. We demonstrate that models trained on DeepMath-103K achieve
  significant improvements on challenging mathematical benchmarks, validating its
  effectiveness. We release DeepMath-103K publicly to facilitate community
  progress in building more capable AI reasoning systems:
  https://github.com/zwhe99/DeepMath.
paper_authors._id: 67ff79b3d68757d92e9c168e
paper_authors.name: Zhiwei He
paper_authors._id: 67ff79b3d68757d92e9c168f
paper_authors.name: Tian Liang
paper_authors._id: 67ff79b3d68757d92e9c1690
paper_authors.name: Jiahao Xu
paper_authors._id: 67ff79b3d68757d92e9c1691
paper_authors.name: Qiuzhi Liu
paper_authors._id: 67ff79b3d68757d92e9c1692
paper_authors.name: Xingyu Chen
paper_authors._id: 67ff79b3d68757d92e9c1693
paper_authors.name: Yue Wang
paper_authors._id: 67ff79b3d68757d92e9c1694
paper_authors.name: Linfeng Song
paper_authors._id: 67ff79b3d68757d92e9c1695
paper_authors.name: Dian Yu
paper_authors._id: 67ff79b3d68757d92e9c1696
paper_authors.name: Zhenwen Liang
paper_authors._id: 67ff79b3d68757d92e9c1697
paper_authors.name: Wenxuan Wang
paper_authors._id: 67ff79b3d68757d92e9c1698
paper_authors.name: Zhuosheng Zhang
paper_authors._id: 67ff79b3d68757d92e9c1699
paper_authors.name: Rui Wang
paper_authors._id: 67ff79b3d68757d92e9c169a
paper_authors.name: Zhaopeng Tu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-09T07:22:52.128Z
paper_authors._id: 67485743561b1e6f9579389f
paper_authors.avatarUrl: /avatars/8a4cc63bd7be388010bc329bb74582a1.svg
paper_authors.fullname: Zhaopeng Tu
paper_authors.type: user
paper_authors.user: zptu
paper_authors._id: 67ff79b3d68757d92e9c169b
paper_authors.name: Haitao Mi
paper_authors._id: 67ff79b3d68757d92e9c169c
paper_authors.name: Dong Yu
paper_title: DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and
    Verifiable Mathematical Dataset for Advancing Reasoning
paper_summary: The capacity for complex mathematical reasoning is a key benchmark for
  artificial intelligence. While reinforcement learning (RL) applied to LLMs
  shows promise, progress is significantly hindered by the lack of large-scale
  training data that is sufficiently challenging, possesses verifiable answer
  formats suitable for RL, and is free from contamination with evaluation
  benchmarks. To address these limitations, we introduce DeepMath-103K, a new,
  large-scale dataset comprising approximately 103K mathematical problems,
  specifically designed to train advanced reasoning models via RL. DeepMath-103K
  is curated through a rigorous pipeline involving source analysis, stringent
  decontamination against numerous benchmarks, and filtering for high difficulty
  (primarily Levels 5-9), significantly exceeding existing open resources in
  challenge. Each problem includes a verifiable final answer, enabling rule-based
  RL, and three distinct R1-generated solutions suitable for diverse training
  paradigms like supervised fine-tuning or distillation. Spanning a wide range of
  mathematical topics, DeepMath-103K promotes the development of generalizable
  reasoning. We demonstrate that models trained on DeepMath-103K achieve
  significant improvements on challenging mathematical benchmarks, validating its
  effectiveness. We release DeepMath-103K publicly to facilitate community
  progress in building more capable AI reasoning systems:
  https://github.com/zwhe99/DeepMath.
paper_ai_summary: A large-scale dataset, DeepMath-103K, comprising complex mathematical problems is introduced to enhance reinforcement learning-driven reasoning in models.
paper_ai_keywords: reinforcement learning R1-generated solutions supervised fine-tuning distillation generalizable reasoning