DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913854352523264 |
|---|---|
| author | He, Zhiwei Liang, Tian Xu, Jiahao Liu, Qiuzhi Chen, Xingyu Wang, Yue Song, Linfeng Yu, Dian Liang, Zhenwen Wang, Wenxuan Zhang, Zhuosheng Wang, Rui Tu, Zhaopeng Mi, Haitao Yu, Dong |
| author_facet | He, Zhiwei Liang, Tian Xu, Jiahao Liu, Qiuzhi Chen, Xingyu Wang, Yue Song, Linfeng Yu, Dian Liang, Zhenwen Wang, Wenxuan Zhang, Zhuosheng Wang, Rui Tu, Zhaopeng Mi, Haitao Yu, Dong |
| contents | Reinforcement learning (RL) with large language models shows promise in complex reasoning. However, its progress is hindered by the lack of large-scale training data that is sufficiently challenging, contamination-free and verifiable. To this end, we introduce DeepMath-103K, a large-scale mathematical dataset designed with high difficulty (primarily levels 5-9), rigorous decontamination against numerous benchmarks, and verifiable answers for rule-based RL reward. It further includes three distinct R1 solutions adaptable for diverse training paradigms such as supervised fine-tuning (SFT). Spanning a wide range of mathematical topics, DeepMath-103K fosters the development of generalizable and advancing reasoning. Notably, models trained on DeepMath-103K achieve state-of-the-art results on challenging mathematical benchmarks and demonstrate generalization beyond math such as biology, physics and chemistry, underscoring its broad efficacy. Data: https://huggingface.co/datasets/zwhe99/DeepMath-103K. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11456 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning He, Zhiwei Liang, Tian Xu, Jiahao Liu, Qiuzhi Chen, Xingyu Wang, Yue Song, Linfeng Yu, Dian Liang, Zhenwen Wang, Wenxuan Zhang, Zhuosheng Wang, Rui Tu, Zhaopeng Mi, Haitao Yu, Dong Computation and Language Artificial Intelligence Reinforcement learning (RL) with large language models shows promise in complex reasoning. However, its progress is hindered by the lack of large-scale training data that is sufficiently challenging, contamination-free and verifiable. To this end, we introduce DeepMath-103K, a large-scale mathematical dataset designed with high difficulty (primarily levels 5-9), rigorous decontamination against numerous benchmarks, and verifiable answers for rule-based RL reward. It further includes three distinct R1 solutions adaptable for diverse training paradigms such as supervised fine-tuning (SFT). Spanning a wide range of mathematical topics, DeepMath-103K fosters the development of generalizable and advancing reasoning. Notably, models trained on DeepMath-103K achieve state-of-the-art results on challenging mathematical benchmarks and demonstrate generalization beyond math such as biology, physics and chemistry, underscoring its broad efficacy. Data: https://huggingface.co/datasets/zwhe99/DeepMath-103K. |
| title | DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2504.11456 |