A Survey of Reinforcement Learning for Large Reasoning Models

TL;DR

This survey reviews recent progress in applying Reinforcement Learning to enhance reasoning capabilities of Large Language Models, highlighting challenges and future directions for scaling towards Artificial SuperIntelligence.

Contribution

It provides a comprehensive overview of RL techniques for reasoning with LLMs and LRMs, analyzing recent developments, challenges, and future research opportunities.

Findings

RL has significantly advanced reasoning in LLMs.

Scaling RL for LRMs faces computational and algorithmic challenges.

The survey identifies key research directions for future development.

Abstract

In this paper, we survey recent advances in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs). RL has achieved remarkable success in advancing the frontier of LLM capabilities, particularly in addressing complex logical tasks such as mathematics and coding. As a result, RL has emerged as a foundational methodology for transforming LLMs into LRMs. With the rapid progress of the field, further scaling of RL for LRMs now faces foundational challenges not only in computational resources but also in algorithm design, training data, and infrastructure. To this end, it is timely to revisit the development of this domain, reassess its trajectory, and explore strategies to enhance the scalability of RL toward Artificial SuperIntelligence (ASI). In particular, we examine research applying RL to LLMs and LRMs for reasoning abilities, especially since the release of DeepSeek-R1, including foundational components, core problems, training resources, and downstream applications, to identify future opportunities and directions for this rapidly evolving area. We hope this review will promote future research on RL for broader reasoning models. Github: https://github.com/TsinghuaC3I/Awesome-RL-for-LRMs