title: Skywork Open Reasoner 1 Technical Report
summary: 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.
paper_authors._id: 6837c342cd1601f5bd670255
paper_authors.name: Jujie He
paper_authors._id: 6837c342cd1601f5bd670256
paper_authors.name: Jiacai Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:42:28.696Z
paper_authors._id: 65643645b09c0b9ece1b8f0e
paper_authors.avatarUrl: /avatars/d5197103b6e92f765bfda7ed2cc8d53e.svg
paper_authors.fullname: Jiacai Liu
paper_authors.type: user
paper_authors.user: skydownacai
paper_authors._id: 6837c342cd1601f5bd670257
paper_authors.name: Chris Yuhao Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:42:32.117Z
paper_authors._id: 658229ef5f6d83438257fce5
paper_authors.avatarUrl: /avatars/b4417de9a338e95dc69cc547a46348e8.svg
paper_authors.fullname: Chris (Yuhao) Liu
paper_authors.type: user
paper_authors.user: chrisliu298
paper_authors._id: 6837c342cd1601f5bd670258
paper_authors.name: Rui Yan
paper_authors._id: 6837c342cd1601f5bd670259
paper_authors.name: Chaojie Wang
paper_authors._id: 6837c342cd1601f5bd67025a
paper_authors.name: Peng Cheng
paper_authors._id: 6837c342cd1601f5bd67025b
paper_authors.name: Xiaoyu Zhang
paper_authors._id: 6837c342cd1601f5bd67025c
paper_authors.name: Fuxiang Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:50:42.361Z
paper_authors._id: 64054d8a3d49e1e066bfa32b
paper_authors.avatarUrl: /avatars/9044f937145cc5aa4bc3a5ffa751f724.svg
paper_authors.fullname: Fuxiang Zhang
paper_authors.type: user
paper_authors.user: sicer
paper_authors._id: 6837c342cd1601f5bd67025d
paper_authors.name: Jiacheng Xu
paper_authors._id: 6837c342cd1601f5bd67025e
paper_authors.name: Wei Shen
paper_authors._id: 6837c342cd1601f5bd67025f
paper_authors.name: Siyuan Li
paper_authors._id: 6837c342cd1601f5bd670260
paper_authors.name: Liang Zeng
paper_authors._id: 6837c342cd1601f5bd670261
paper_authors.name: Tianwen Wei
paper_authors._id: 6837c342cd1601f5bd670262
paper_authors.name: Cheng Cheng
paper_authors._id: 6837c342cd1601f5bd670263
paper_authors.name: Bo An
paper_authors._id: 6837c342cd1601f5bd670264
paper_authors.name: Yang Liu
paper_authors._id: 6837c342cd1601f5bd670265
paper_authors.name: Yahui Zhou
paper_title: Skywork Open Reasoner 1 Technical Report
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: reinforcement learning LLMs Chain-of-Thought Skywork-OR1 DeepSeek-R1-Distill AIME24 AIME25 LiveCodeBench entropy collapse ablation studies