DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning.

Area of Science:

• Artificial Intelligence
• Machine Learning

Background:

• General reasoning is a core challenge in AI.
• Large language models (LLMs) and chain-of-thought (CoT) prompting show promise but require extensive human data.
• Current LLM capabilities are insufficient for complex reasoning tasks.

Purpose of the Study:

• To demonstrate that pure reinforcement learning (RL) can enhance LLM reasoning abilities.
• To obviate the need for human-annotated reasoning trajectories.
• To facilitate the emergent development of advanced reasoning patterns in LLMs.

Main Methods:

• Implementing a pure reinforcement learning (RL) framework for LLMs.
• Training LLMs using RL to incentivize emergent reasoning patterns.
• Evaluating the performance of RL-trained LLMs on verifiable tasks.

Main Results:

• The RL framework facilitated emergent reasoning patterns like self-reflection and verification.
• LLMs trained with RL surpassed supervised learning counterparts on mathematics, coding, and STEM tasks.
• Emergent reasoning patterns from large models can enhance smaller models' capabilities.

Conclusions:

• Pure reinforcement learning effectively enhances LLM reasoning without human demonstrations.
• RL-trained LLMs exhibit superior performance on complex, verifiable tasks.
• The developed RL framework offers a scalable method for advancing AI reasoning and can guide smaller models.