"# Summary of \"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning\"\n",
"\n",
"## Overview\n",
"The paper introduces **DeepSeek-R1**, a first-generation reasoning model developed by DeepSeek-AI. The model is designed to enhance reasoning capabilities in large language models (LLMs) using reinforcement learning (RL). Two versions are presented:\n",
"- **DeepSeek-R1-Zero**: A model trained via large-scale RL without supervised fine-tuning (SFT), showcasing strong reasoning abilities but facing challenges like poor readability and language mixing.\n",
"- **DeepSeek-R1**: An improved version incorporating multi-stage training and cold-start data before RL, achieving performance comparable to OpenAI's models on reasoning tasks.\n",
"\n",
"## Key Contributions\n",
"- Open-sourcing of **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 architectures.\n",
"- The models are made available to support the research community.\n",
"\n",
"## Community Engagement\n",
"- The paper has been widely discussed and recommended, with 216 upvotes and 45 models citing it.\n",
"- Additional resources, including a video review and articles, are available through external links provided by the community.\n",
"\n",
"## Related Research\n",
"The paper is part of a broader trend in enhancing LLMs' reasoning abilities, with related works such as:\n",
"- **Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization (2024)**\n",
"- **Offline Reinforcement Learning for LLM Multi-Step Reasoning (2024)**\n",
"- **Reasoning Language Models: A Blueprint (2025)**\n",
"\n",
"## Availability\n",
"- The paper and models are accessible on [GitHub](https://github.com/deepseek-ai/DeepSeek-R1) and the [arXiv page](https://arxiv.org/abs/2501.12948).\n",