DeepSeek-R1: Reinforcing LLM Reasoning Through Self-Evolution

This paper published on Nature on September 17 2025, "DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning," details the development of DeepSeek-R1-Zero and DeepSeek-R1, two large language models (LLMs) engineered to enhance reasoning capabilities. The authors explain how reinforcement learning (RL) is used to enable emergent advanced reasoning patterns like self-reflection and dynamic strategy adaptation, moving beyond reliance on human-annotated data. The paper discusses a multistage training pipeline for DeepSeek-R1, integrating rejection sampling, RL, and supervised fine-tuning to improve both reasoning and general language tasks while addressing issues like language mixing. Furthermore, the researchers highlight the release of these models and their distilled, smaller versions to the public to contribute to ongoing AI research. Ultimately, the source concludes by acknowledging the ethical considerations and limitations of their pure RL methodology, such as reward hacking and token efficiency.Source:https://www.nature.com/articles/s41586-025-09422-z

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