Skywork Open Reasoner 1 Technical Report
The success of DeepSeek-R1 underscores the significant role of reinforcement
learning (RL) in enhancing the reasoning capabilities of large language models
(LLMs). In this work, we present Skywork-OR1, an effective and scalable RL
implementation for long Chain-of-Thought (CoT) models. Building on the
DeepSeek-R1-Distill model series, our RL approach achieves notable performance
gains, increasing average accuracy across AIME24, AIME25, and LiveCodeBench
from 57.8% to 72.8% (+15.0%) for the 32B model and from 43.6% to 57.5% (+13.9%)
for the 7B model. Our Skywork-OR1-32B model surpasses both DeepSeek-R1 and
Qwen3-32B on the AIME24 and AIME25 benchmarks, while achieving comparable
results on LiveCodeBench. The Skywork-OR1-7B and Skywork-OR1-Math-7B models
demonstrate competitive reasoning capabilities among models of similar size. We
perform comprehensive ablation studies on the core components of our training
pipeline to validate their effectiveness. Additionally, we thoroughly
investigate the phenomenon of entropy collapse, identify key factors affecting
entropy dynamics, and demonstrate that mitigating premature entropy collapse is
critical for improved test performance. To support community research, we fully
open-source our model weights, training code, and training datasets.
6837c342cd1601f5bd670255
Jujie He
6837c342cd1601f5bd670256
Jiacai Liu
claimed_verified
2025-05-29T07:42:28.696Z
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Jiacai Liu
user
skydownacai
6837c342cd1601f5bd670257
Chris Yuhao Liu
claimed_verified
2025-05-29T07:42:32.117Z
658229ef5f6d83438257fce5
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Chris (Yuhao) Liu
user
chrisliu298
6837c342cd1601f5bd670258
Rui Yan
6837c342cd1601f5bd670259
Chaojie Wang
6837c342cd1601f5bd67025a
Peng Cheng
6837c342cd1601f5bd67025b
Xiaoyu Zhang
6837c342cd1601f5bd67025c
Fuxiang Zhang
claimed_verified
2025-06-03T08:50:42.361Z
64054d8a3d49e1e066bfa32b
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Fuxiang Zhang
user
sicer
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Jiacheng Xu
6837c342cd1601f5bd67025e
Wei Shen
6837c342cd1601f5bd67025f
Siyuan Li
6837c342cd1601f5bd670260
Liang Zeng
claimed_verified
2025-06-25T08:09:49.515Z
6621efe1a6eec3ad03e38759
/avatars/c35acce69f244ec0833dffd53eedf6a3.svg
Liang Zeng
user
zengliangcs
6837c342cd1601f5bd670261
Tianwen Wei
6837c342cd1601f5bd670262
Cheng Cheng
6837c342cd1601f5bd670263
Bo An
6837c342cd1601f5bd670264
Yang Liu
6837c342cd1601f5bd670265
Yahui Zhou
Skywork Open Reasoner 1 Technical Report
The success of DeepSeek-R1 underscores the significant role of reinforcement
learning (RL) in enhancing the reasoning capabilities of large language models
(LLMs). In this work, we present Skywork-OR1, an effective and scalable RL
implementation for long Chain-of-Thought (CoT) models. Building on the
DeepSeek-R1-Distill model series, our RL approach achieves notable performance
gains, increasing average accuracy across AIME24, AIME25, and LiveCodeBench
from 57.8% to 72.8% (+15.0%) for the 32B model and from 43.6% to 57.5% (+13.9%)
for the 7B model. Our Skywork-OR1-32B model surpasses both DeepSeek-R1 and
Qwen3-32B on the AIME24 and AIME25 benchmarks, while achieving comparable
results on LiveCodeBench. The Skywork-OR1-7B and Skywork-OR1-Math-7B models
demonstrate competitive reasoning capabilities among models of similar size. We
perform comprehensive ablation studies on the core components of our training
pipeline to validate their effectiveness. Additionally, we thoroughly
investigate the phenomenon of entropy collapse, identify key factors affecting
entropy dynamics, and demonstrate that mitigating premature entropy collapse is
critical for improved test performance. To support community research, we fully
open-source our model weights, training code, and training datasets.
Skywork-OR1 is a reinforcement learning approach for long Chain-of-Thought models that improves accuracy over DeepSeek-R1 across various benchmarks by addressing entropy collapse.
reinforcement learning LLMs Chain-of-Thought Skywork-OR1 DeepSeek-R1-Distill AIME24 AIME25 LiveCodeBench entropy collapse ablation studies