Research PaperPublished: January 2025~2,450 citations recorded

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Authors: DeepSeek-AI Team

Institution / Lab: DeepSeek AI Research Lab

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Paper Abstract & Theoretical Contribution

Pioneered pure reinforcement learning without supervised fine-tuning (DeepSeek-R1-Zero), proving that large language models naturally develop self-verification, chain-of-thought exploration, and AHA-moments when incentivized with outcome-based rewards.

Key Experimental Findings & Benchmarks

Demonstrated that Large-Scale Reinforcement Learning induces reasoning behaviors natively.

Achieved 97.3% on MATH-500 benchmark, rivaling OpenAI o1.

Released open-weights distilled models (1.5B to 70B) powering accessible sovereign AI research worldwide.

Taxonomy & Field Classification:

#Reinforcement Learning#Test-Time Compute#Reasoning Models#Open Weights

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