README.md

metadata

base_model:
  - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

🤔 Skywork-OR1 (Open Reasoner 1)

✊ Unleashing the Power of Reinforcement Learning for Math and Code Reasoners 🤖

🔥 News

• April 13, 2025: We release the Skywork-OR1 (Open Reasoner 1) series of models, including Skywork-OR1-Math-7B, Skywork-OR1-32B-Preview, and Skywork-OR1-7B-Preview. We open-source
  • 🤗 Model weights: Skywork-OR1-Math-7B, Skywork-OR1-32B-Preview, Skywork-OR1-7B-Preview
  • 🤗 Training data: Skywork-OR1-RL-Data
  • 🧑‍💻 Code: Skywork-OR1
  • We also release a Notion Blog to share detailed training recipes and extensive experimental results, analysis, and insights, dedicated to helping the community to better research, understand, and push the frontier of open reasoning models.

📖 Overview

_{The AIME24 scores versus training steps of Skywork-OR1-Math-7B in our multi-stage training pipeline.}

The Skywork-OR1 (Open Reasoner 1) model series consists of powerful math and code reasoning models trained using large-scale rule-based reinforcement learning with carefully designed datasets and training recipes. This series includes two general-purpose reasoning modelsl, Skywork-OR1-7B-Preview and Skywork-OR1-32B-Preview, along with a math-specialized model, Skywork-OR1-Math-7B.

• Skywork-OR1-Math-7B is specifically optimized for mathematical reasoning, scoring 69.8 on AIME24 and 52.3 on AIME25 — well ahead of all models of similar size.
• Skywork-OR1-32B-Preview delivers the 671B-parameter Deepseek-R1 performance on math tasks (AIME24 and AIME25) and coding tasks (LiveCodeBench).
• Skywork-OR1-7B-Preview outperforms all similarly sized models in both math and coding scenarios.

📊 Evaluation

We evaluate our models on AIME24, AIME25, and LiveCodeBench. Instead of using Pass@1, which is common in prior work, we introduce Avg@K as the primary metric. This metric robustly measures a model's average performance across K independent attempts, reducing the impact of randomness and enhancing the reliability of the results. We believe that Avg@K provides a better reflection of a model's stability and reasoning consistency.

We include the detailed results in the following table.

Model	AIME24 (Avg@32)	AIME25 (Avg@32)	LiveCodeBench (8/1/24-2/1/25) (Avg@4)
DeepSeek-R1-Distill-Qwen-7B	55.5	39.2	37.6
Light-R1-7B-DS	59.1	44.3	39.5
DeepSeek-R1-Distill-Qwen-32B	72.9	59.0	57.2
TinyR1-32B-Preview	78.1	65.3	61.6
QwQ-32B	79.5	65.3	61.6
DeepSeek-R1	79.8	70.0	65.9
Skywork-OR1-Math-7B	69.8	52.3	43.6
Skywork-OR1-7B-Preview	63.6	45.8	43.9
Skywork-OR1-32B-Preview	79.7	69.0	63.9

⚙️ Training Recipe

We offer a brief overview of our data and training pipeline below. For more details, please refer to our Notion Blog here.

Data

• We select, clean, and curate a dataset of 110K verifiable, challenging, and diverse math problems and 14K coding questions from open-source datasets.
• We perform model-aware difficulty estimation for each problem and model and conduct rigorous quality assessment prior to training to ensure training efficiency and effectiveness.

Training

We develop a customized version of GRPO that leverages both data-wise and training-wise improvements:

• We perform both offline and online difficulty-based filtering and rejection sampling to improve training efficiency.
• We incorporate a multi-stage training pipeline coupled with adaptive entropy control and other techniques to enhance exploration and stability.

📄 Technical Report

Our technical report will be released soon. Stay tuned!

🙏 Acknowledgements

• Both of our models are trained on top of DeepSeek-R1-Distill-Qwen-7B and DeepSeek-R1-Distill-Qwen-32B.
• Both models are trained using a custom fork of the wonderful verl project.

📚 Citation

We will update the citation once the technical report is released. In the meantime, please cite the following:

@misc{skywork-or1-2025,
  title={},
  author={},
  howpublished={\url{https://capricious-hydrogen-41c.notion.site/Skywork-Open-Reaonser-Series-1d0bc9ae823a80459b46c149e4f51680}},
  note={Notion Blog},
  year={2025}
}