- April 13, 2025: We release the
Skywork-OR1(Open Reasoner 1) series of models, includingSkywork-OR1-Math-7B,Skywork-OR1-32B-Preview, andSkywork-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(Coming Soon) - 🧑💻 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.
- 🤗 Model weights:
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 models—Skywork-OR1-7B-Preview and Skywork-OR1-32B-Preview—along with a math-specialized model, Skywork-OR1-Math-7B.
Skywork-OR1-Math-7Bis 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-Previewdelivers the 671B-parameter Deepseek-R1 performance on math tasks (AIME24 and AIME25) and coding tasks (LiveCodeBench).Skywork-OR1-7B-Previewoutperforms all similarly sized models in both math and coding scenarios.
The final release version will be available in two weeks.
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 |
Docker environment:
docker pull whatcanyousee/verl:vemlp-th2.4.0-cu124-vllm0.6.3-ray2.10-te2.0-megatron0.11.0-v0.0.6
# Launch the desired Docker image:
docker run --runtime=nvidia -it --rm --shm-size="10g" --cap-add=SYS_ADMIN -v <image:tag>
# Inside the container, install Skywork-OR1
git clone https://github.com/SkyworkAI/Skywork-OR1.git && cd Skywork-OR1 && pip3 install -e .Conda environment:
# Installing Python 3.10 Environment.
conda create -n verl python==3.10
conda activate verl
# Installing RLLM dependencies.
pip3 install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu124
pip3 install flash-attn --no-build-isolation
git clone https://github.com/SkyworkAI/Skywork-OR1.git
cd Skywork-OR1
pip3 install -e .Training scripts are currently being organized and will be available in 1-2 days. Please stay tuned.
We provide evaluation scripts to reproduce the results of the Skywork-OR1-Series.
Evaluation data for AIME24 and AIME25 is already available in our GitHub repository.
# Evalation AIME24
MODEL_PATH=Skywork/Skywork-OR1-32B-Preview \
DATA_PATH=or1_data/eval/aime24.parquet \
SAMPLES=32 \
TASK_NAME=Aime24_Avg$SAMPLES-Skywork_OR1_Math_7B \
bash ./or1_script/eval/eval_32b.sh
# Evalation AIME25
MODEL_PATH=Skywork/Skywork-OR1-Math-7B \
DATA_PATH=or1_data/eval/aime25.parquet \
SAMPLES=32 \
TASK_NAME=Aime25_Avg$SAMPLES-Skywork_OR1_Math_7B \
bash ./or1_script/eval/eval_7b.shFor Livecodebench, please download the data from Hugging Face.
# Download LCB
huggingface-cli download Skywork/LiveCodeBench --repo-type=dataset --local-dir or1_data/eval/livecodebench
unzip or1_data/eval/livecodebench/livecodebench.zip -d or1_data/eval/livecodebench/
mv or1_data/eval/livecodebench/livecodebench/* or1_data/eval/livecodebench/
# Evalation LCB
MODEL_PATH=Skywork/Skywork-OR1-Math-7B \
DATA_PATH=or1_data/eval/livecodebench/livecodebench_2408_2502.parquet \
SAMPLES=4 \
TASK_NAME=LiveCodeBench_Avg$SAMPLES-Skywork_OR1_Math_7B \
bash ./or1_script/eval/eval_7b.shOur technical report will be released soon. Stay tuned!
- Both of our models are trained on top of
DeepSeek-R1-Distill-Qwen-7BandDeepSeek-R1-Distill-Qwen-32B. - Both models are trained using a custom fork of the wonderful
verlproject.
We will update the citation once the technical report is released. In the meantime, please cite the following:
@misc{skywork-or1-2025,
title={Skywork Open Reaonser Series},
author = {He, Jujie and Liu, Jiacai and Liu, Chris Yuhao and Yan, Rui and Wang, Chaojie and Cheng, Peng and Zhang, Xiaoyu and Zhang, Fuxiang and Xu, Jiacheng and Shen, Wei and Li, Siyuan and Zeng, Liang and Wei, Tianwen and Cheng, Cheng and An, Bo and Liu, Yang and Zhou, Yahui},
howpublished={\url{https://capricious-hydrogen-41c.notion.site/Skywork-Open-Reaonser-Series-1d0bc9ae823a80459b46c149e4f51680}},
note={Notion Blog},
year={2025}
}