title: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via
    Reinforcement Learning
summary: We introduce our first-generation reasoning models, DeepSeek-R1-Zero and
  DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement
  learning (RL) without supervised fine-tuning (SFT) as a preliminary step,
  demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero
  naturally emerges with numerous powerful and intriguing reasoning behaviors.
  However, it encounters challenges such as poor readability, and language
  mixing. To address these issues and further enhance reasoning performance, we
  introduce DeepSeek-R1, which incorporates multi-stage training and cold-start
  data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217
  on reasoning tasks. To support the research community, we open-source
  DeepSeek-R1-Zero, DeepSeek-R1, and six dense models (1.5B, 7B, 8B, 14B, 32B,
  70B) distilled from DeepSeek-R1 based on Qwen and Llama.
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paper_authors.name: Zijun Liu
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paper_authors.fullname: Zijun Liu
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paper_authors._id: 6791b70a76d05e183a411658
paper_authors.name: Zilin Li
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paper_authors.name: Ziwei Xie
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paper_authors.name: Ziyang Song
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paper_authors.name: Zizheng Pan
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paper_authors.name: Zhen Huang
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paper_authors.name: Zhipeng Xu
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paper_authors.name: Zhongyu Zhang
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paper_authors.name: Zhen Zhang
paper_title: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via
    Reinforcement Learning
paper_summary: We introduce our first-generation reasoning models, DeepSeek-R1-Zero and
  DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement
  learning (RL) without supervised fine-tuning (SFT) as a preliminary step,
  demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero
  naturally emerges with numerous powerful and intriguing reasoning behaviors.
  However, it encounters challenges such as poor readability, and language
  mixing. To address these issues and further enhance reasoning performance, we
  introduce DeepSeek-R1, which incorporates multi-stage training and cold-start
  data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217
  on reasoning tasks. To support the research community, we open-source
  DeepSeek-R1-Zero, DeepSeek-R1, and six dense models (1.5B, 7B, 8B, 14B, 32B,
  70B) distilled from DeepSeek-R1 based on Qwen and Llama.
paper_ai_keywords: reinforcement learning (RL) supervised fine-tuning (SFT) reasoning behaviors medical reasoning multi-stage training cold-start data OpenAI-o1-1217 reasoning tasks Qwen Llama