---
title: "DeepMath-103K — paper by He et al. (2025)"
description: "Training LLMs to reason with reinforcement learning from verifiable rewards (RLVR) needs math… 48 references, cited by 77 papers here."
authors: ["Zhiwei He", "Tian Liang", "Jiahao Xu", "Qiuzhi Liu", "Xingyu Chen", "Yue Wang", "Linfeng Song", "Dian Yu", "Zhenwen Liang", "Wenxuan Wang", "Zhuosheng Zhang", "Rui Wang", "Zhaopeng Tu", "Haitao Mi", "Dong Yu"]
year: 2025
venue: "arXiv:2504.11456"
source: "https://arxiv.org/abs/2504.11456"
doi: "10.48550/arxiv.2504.11456"
arxiv: "2504.11456"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/he-2025-deepmath-103k-large-scale-challenging/"
---
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# DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

**Authors:** [Zhiwei He](https://md.research.elamur.ai/free/en/authors/zhiwei-he/), [Tian Liang](https://md.research.elamur.ai/free/en/authors/tian-liang/), [Jiahao Xu](https://md.research.elamur.ai/free/en/authors/jiahao-xu/), [Qiuzhi Liu](https://md.research.elamur.ai/free/en/authors/qiuzhi-liu/), [Xingyu Chen](https://md.research.elamur.ai/free/en/authors/xingyu-chen/), [Yue Wang](https://md.research.elamur.ai/free/en/authors/yue-wang/), [Linfeng Song](https://md.research.elamur.ai/free/en/authors/linfeng-song/), [Dian Yu](https://md.research.elamur.ai/free/en/authors/dian-yu/), [Zhenwen Liang](https://md.research.elamur.ai/free/en/authors/zhenwen-liang/), [Wenxuan Wang](https://md.research.elamur.ai/free/en/authors/wenxuan-wang/), [Zhuosheng Zhang](https://md.research.elamur.ai/free/en/authors/zhuosheng-zhang/), [Rui Wang](https://md.research.elamur.ai/free/en/authors/rui-wang/), [Zhaopeng Tu](https://md.research.elamur.ai/free/en/authors/zhaopeng-tu/), [Haitao Mi](https://md.research.elamur.ai/free/en/authors/haitao-mi/) and [Dong Yu](https://md.research.elamur.ai/free/en/authors/dong-yu/) · **Year:** 2025 · **Venue:** arXiv:2504.11456 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2504.11456) · [PDF](https://arxiv.org/pdf/2504.11456) · [DOI 10.48550/arxiv.2504.11456](https://doi.org/10.48550/arxiv.2504.11456) · [arXiv 2504.11456](https://arxiv.org/abs/2504.11456) · [OpenAlex](https://openalex.org/W4416052877)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [77](https://md.research.elamur.ai/free/en/papers/he-2025-deepmath-103k-large-scale-challenging/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Training LLMs to reason with reinforcement learning from verifiable rewards (RLVR) needs math problems that are hard, have checkable answers, and do not overlap with evaluation benchmarks. Existing open collections usually fall short on at least one of these and often reuse the same well-formatted sources such as AIME. The authors built DeepMath-103K by mining less structured material, especially Math StackExchange threads, along with training splits of open sets like MMIQC, WebInstSub and NuminaMath-CoT. They converted this material into question–answer pairs and filtered for difficulty. They also removed overlaps with many test sets and kept only answers that rules can verify. Each item carries a difficulty score, a hierarchical topic label, and three DeepSeek-R1 solutions for SFT use. Models trained with RL on the data, the DeepMath series, set new open-model highs on AIME and related benchmarks and also improve on science questions in GPQA-Diamond. The dataset, code and weights are public, giving the community a cleaner and harder resource for reasoning research.

## Key points
- **Composition:** about 95K newly curated problems at difficulty levels 5–9, plus 8K easier items (levels 3–5) taken from SimpleRL. The dataset is released at `zwhe99/DeepMath-103K` on Hugging Face.
- **Contamination in raw sources:** before cleaning, the raw pool contained roughly 90% of AIME24 and AMC23, 76.6% of MATH500, and about a third of Minerva Math and OlympiadBench. A string-similarity check against MATH500 found leaks in most competing RL datasets but none in DeepMath-103K.
- **Zero-RL results:** starting from base models, DeepMath-Zero-7B gains +12.7 on AIME24 and +12.1 on AIME25 in pass@1. The variant built on Qwen-2.5-Math-7B gains +23.0 and +19.1.
- **RL results:** DeepMath-Omn-1.5B, built on OpenMath-Nemotron-1.5B, scores 64.0 on AIME24 and 57.3 on AIME25, edging past o1-mini (63.6 on AIME24). Training used the verl framework.
- **SFT and limitations:** SFT of Qwen-2.5-7B on all three R1 solutions helps but trails RL (AIME24: 13.8 vs 20.4). Difficulty labels come from LLM judgments, and some multiple-choice or yes/no items can be answered correctly by guessing.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Zhiwei He, Tian Liang, Jiahao Xu, Qiuzhi Liu, Xingyu Chen, Yue Wang, Linfeng Song, Dian Yu, Zhenwen Liang, Wenxuan Wang, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi and Dong Yu. 2025. DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning. arXiv:2504.11456.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{he2025deepmath,
  title = {{DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning}},
  author = {Zhiwei He, Tian Liang, Jiahao Xu, Qiuzhi Liu, Xingyu Chen, Yue Wang, Linfeng Song, Dian Yu, Zhenwen Liang, Wenxuan Wang, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi and Dong Yu},
  year = {2025},
  doi = {10.48550/arxiv.2504.11456},
  eprint = {2504.11456},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2504.11456}
}
```

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*Updated 2 November 2026*