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

Understand

General reasoning represents a long-standing and formidable challenge in artificial intelligence.

• Recent breakthroughs, exemplified by large language models (LLMs) and chain-of-thought prompting, have achieved considerable success on foundational reasoning tasks.
• However, this success is heavily contingent upon extensive human-annotated demonstrations, and models' capabilities are still insufficient for more complex problems.
• Here we show that the reasoning abilities of LLMs can be incentivized through pure reinforcement learning (RL), obviating the need for human-labeled reasoning trajectories.