section_path: Overview of models/R1
section_name: R1
paragraph_text: R1
  
  In January 2025, DeepSeek released the DeepSeek-R1 model under the MIT License.
  
  DeepSeek-R1-Lite-Preview was trained for logical inference, mathematical reasoning, and real-time problem-solving. DeepSeek claimed that it exceeded performance of OpenAI o1 on benchmarks such as American Invitational Mathematics Examination (AIME) and MATH. However, The Wall Street Journal reported that on 15 problems from the 2024 edition of AIME, the o1 model reached a solution faster.
  
  DeepSeek-R1 and DeepSeek-R1-Zero were initialized from DeepSeek-V3-Base and share its architecture. DeepSeek-R1-Distill models were instead initialized from other pretrained open-weight models, including LLaMA and Qwen, then fine-tuned on synthetic data generated by R1.
  
  A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>. User: <prompt>. Assistant:
  
  DeepSeek-R1-Zero was trained exclusively using GRPO RL without SFT. Unlike previous versions, it used no model-based reward. All reward functions were rule-based, "mainly" of two types (other types were not specified): accuracy rewards and format rewards. Accuracy reward was checking whether a boxed answer is correct (for math) or whether a code passes tests (for programming). Format reward was checking whether the model puts its thinking trace within a <think>...</think> tag.
  
  R1-Zero has issues with readability and mixing languages. R1 was trained to address these issues and further improve reasoning:
  
  Distilled models were trained by SFT on 800K data synthesized from DeepSeek-R1, in a similar way as step 3. They were not trained with RL.
  
  There were reports that R2, the intended successor to R1, was originally planned for release in early May 2025. However, on 28 May 2025, R1 was instead updated to version R1-0528.