DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and
  Verifiable Mathematical Dataset for Advancing Reasoning
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.
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Zhiwei He
67ff79b3d68757d92e9c168f
Tian Liang
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Jiahao Xu
67ff79b3d68757d92e9c1691
Qiuzhi Liu
67ff79b3d68757d92e9c1692
Xingyu Chen
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Yue Wang
67ff79b3d68757d92e9c1694
Linfeng Song
claimed_verified
2025-07-11T08:09:22.778Z
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Linfeng Song
user
freesunshine0316
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Dian Yu
claimed_verified
2025-07-18T07:51:01.766Z
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Dian Yu
user
yudian
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Zhenwen Liang
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Wenxuan Wang
67ff79b3d68757d92e9c1698
Zhuosheng Zhang
67ff79b3d68757d92e9c1699
Rui Wang
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Zhaopeng Tu
claimed_verified
2025-05-09T07:22:52.128Z
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/avatars/8a4cc63bd7be388010bc329bb74582a1.svg
Zhaopeng Tu
user
zptu
67ff79b3d68757d92e9c169b
Haitao Mi
67ff79b3d68757d92e9c169c
Dong Yu
DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and
  Verifiable Mathematical Dataset for Advancing Reasoning
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.
A large-scale dataset, DeepMath-103K, comprising complex mathematical problems is introduced to enhance reinforcement learning-driven reasoning in models.
reinforcement learning R1-generated solutions supervised fine-tuning distillation generalizable reasoning