- May 8, 2025: We found that 48 samples contained hints that revealed the answers. The relevant questions have now been revised to remove the leaked answers.
- April 14, 2025: We release
DeepMath-103K, a large-scale dataset featuring challenging, verifiable, and decontaminated math problems tailored for RL and SFT. We open source:- 🤗 Training data:
DeepMath-103K - 🤗 Model weights:
DeepMath-Zero-7B,DeepMath-1.5B - 💻 Code:
DeepMath - 📝 Paper detailing data curation:
arXiv:2504.11456
- 🤗 Training data:
DeepMath-103K is meticulously curated to push the boundaries of mathematical reasoning in language models. Key features include:
1. Challenging Problems: DeepMath-103K has a strong focus on difficult mathematical problems (primarily Levels 5-9), significantly raising the complexity bar compared to many existing open datasets.
Difficulty distribution comparison.
2. Broad Topical Diversity: The dataset spans a wide spectrum of mathematical subjects, including Algebra, Calculus, Number Theory, Geometry, Probability, and Discrete Mathematics.
Hierarchical breakdown of mathematical topics covered in DeepMath-103K.
3. Rigorous Decontamination: Built from diverse sources, the dataset underwent meticulous decontamination against common benchmarks using semantic matching. This minimizes test set leakage and promotes fair model evaluation.
Detected contamination examples. Subtle conceptual overlaps can also be identified.
4. Rich Data Format: Each sample in DeepMath-103K is structured with rich information to support various research applications:
An example data sample from DeepMath-103K.
- Question: The mathematical problem statement.
- Final Answer: A reliably verifiable final answer, enabling robust rule-based reward functions for RL.
- Difficulty: A numerical score for difficulty-aware training or analysis.
- Topic: Hierarchical classification for topic-specific applications.
- R1 Solutions: Three distinct reasoning paths from DeepSeek-R1, valuable for supervised fine-tuning (SFT) or knowledge distillation.
DeepMath-Zero-7B and DeepMath-1.5B are trained on the DeepMath-103K dataset via RL. These models are initialized from Qwen2.5-7B-Base and R1-Distill-Qwen-1.5B, respectively.
| Model | MATH 500 | AMC23 | Olympiad Bench | Minerva Math | AIME24 | AIME25 |
|---|---|---|---|---|---|---|
| Qwen2.5-7B-Base | 54.8 | 35.3 | 27.8 | 16.2 | 7.7 | 5.4 |
| Open-Reasoner-Zero-7B | 81.8 | 58.9 | 47.9 | 38.4 | 15.6 | 14.4 |
| Qwen-2.5-7B-SimpleRL-Zoo | 77.0 | 55.8 | 41.0 | 41.2 | 15.6 | 8.7 |
| DeepMath-Zero-7B | 85.5 | 64.7 | 51.0 | 45.3 | 20.4 | 17.5 |
| Model | MATH 500 | AMC23 | Olympiad Bench | Minerva Math | AIME24 | AIME25 |
|---|---|---|---|---|---|---|
| R1-Distill-Qwen-1.5B | 84.7 | 72.0 | 53.1 | 36.6 | 29.4 | 24.8 |
| DeepScaleR-1.5B-Preview | 89.4 | 80.3 | 60.9 | 42.2 | 42.3 | 29.6 |
| Still-3-1.5B-Preview | 86.6 | 75.8 | 55.7 | 38.7 | 30.8 | 24.6 |
| DeepMath-1.5B | 89.9 | 82.3 | 61.8 | 42.5 | 37.3 | 30.8 |
git clone --recurse-submodules https://github.com/zwhe99/DeepMath.git && cd DeepMath
conda create -y -n deepmath python=3.12.2 && conda activate deepmath
pip3 install ray[default]
pip3 install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
pip3 install flash-attn==2.7.4.post1 --no-build-isolation
pip3 install omegaconf==2.4.0.dev3 hydra-core==1.4.0.dev1 antlr4-python3-runtime==4.11.0 vllm==0.7.3
pip3 install math-verify[antlr4_11_0]==0.7.0 fire deepspeed tensorboardX prettytable datasets transformers==4.49.0
pip3 install -e verlVLLM_ALLOW_LONG_MAX_MODEL_LEN=1 VLLM_ATTENTION_BACKEND=XFORMERS VLLM_USE_V1=1 VLLM_WORKER_MULTIPROC_METHOD=spawn python3 uni_eval.py \
--base_model zwhe99/DeepMath-Zero-7B \
--chat_template_name orz \
--system_prompt_name simplerl \
--output_dir \
--bf16 True \
--tensor_parallel_size 8 \
--data_id zwhe99/MATH \
--split math500 \
--max_model_len 32768 \
--temperature 0.6 \
--top_p 0.95 \
--n 16-
Data Preparation
DATA_DIR=/path/to/your/data python3 verl/examples/data_preprocess/deepmath_103k.py --local_dir $DATA_DIR -
Start Ray
# Head node (×1) ray start --head --port=6379 --node-ip-address=$HEAD_ADDR --num-gpus=8 # Worker nodes (×7) ray start --address=$HEAD_ADDR:6379 --node-ip-address=$WORKER_ADDR --num-gpus=8
-
Launch training at head node. See
scripts/trainfor training scripts.
This work can not be done without the help of the following works:
- verl: A very fast reinforcement learning framework.
- Vivacem/MMIQC: A mixture of question-response pairs extracted from Mathematics Stack Exchange pages.
- TIGER-Lab/WebInstructSub: Instruction data from MathStackExchange and ScienceStackExchange.
- AI-MO/NuminaMath-CoT: Approximately 860k math problems.
@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},
}