DeepMath-103K 是由腾讯以及上海交通大学于 2025 年联合发布的一个用于训练和评估数学推理模型的大规模数据集，相关论文成果为「DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning」。 该数据集重点关注 5-9 级难度的数学问题，涵盖代数、微积分、数论、几何、概率、离散数学等多个领域，侧重挑战复杂推理能力，该数据集还通过语义匹配针对常见基准进行了细致的去污染处理，最大限度减少测试集泄露并促进模型公平评估。

Citation

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