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DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
DeepSeek-AI • 2025
ReasoningReinforcement LearningOpen Source
Abstract
DeepSeek-R1 demonstrated that chain-of-thought reasoning can emerge from pure reinforcement learning without massive supervised fine-tuning. Published in Nature, this paper became one of the most consequential open-source releases in AI history, sparking a global conversation about compute efficiency and the viability of alternative training paradigms.
Why It Matters
- Proved reasoning emerges from RL without supervised fine-tuning
- One of the most impactful open-source AI releases ever
- Reshaped the debate on compute efficiency vs. brute-force scaling
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DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
DeepSeek-AI • 2025
ReasoningReinforcement LearningOpen Source
Abstract
DeepSeek-R1 demonstrated that chain-of-thought reasoning can emerge from pure reinforcement learning without massive supervised fine-tuning. Published in Nature, this paper became one of the most consequential open-source releases in AI history, sparking a global conversation about compute efficiency and the viability of alternative training paradigms.
Why It Matters
- Proved reasoning emerges from RL without supervised fine-tuning
- One of the most impactful open-source AI releases ever
- Reshaped the debate on compute efficiency vs. brute-force scaling
Ask about this paper
Loading chat...