REA-RL: Reflection-Aware Online Reinforcement Learning for Efficient Large Reasoning Models

• URL: http://arxiv.org/abs/2505.19862v1
• Date: Mon, 26 May 2025 11:47:16 GMT
• Title: REA-RL: Reflection-Aware Online Reinforcement Learning for Efficient Large Reasoning Models
• Authors: Hexuan Deng, Wenxiang Jiao, Xuebo Liu, Jun Rao, Min Zhang,
• Abstract summary: Large Reasoning Models (LRMs) demonstrate strong performance in complex tasks but often face the challenge of overthinking.<n>Existing approaches synthesize shorter reasoning responses for LRMs to learn, but are inefficient for online usage due to the time-consuming data generation and filtering processes.<n>We propose REA-RL, which introduces a small reflection model for efficient scaling in online training, offering both parallel sampling and sequential revision.
• Score: 33.05490585699939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Reasoning Models (LRMs) demonstrate strong performance in complex tasks but often face the challenge of overthinking, leading to substantially high inference costs. Existing approaches synthesize shorter reasoning responses for LRMs to learn, but are inefficient for online usage due to the time-consuming data generation and filtering processes. Meanwhile, online reinforcement learning mainly adopts a length reward to encourage short reasoning responses, but tends to lose the reflection ability and harm the performance. To address these issues, we propose REA-RL, which introduces a small reflection model for efficient scaling in online training, offering both parallel sampling and sequential revision. Besides, a reflection reward is designed to further prevent LRMs from favoring short yet non-reflective responses. Experiments show that both methods maintain or enhance performance while significantly improving inference efficiency. Their combination achieves a good balance between performance and efficiency, reducing inference costs by 35% without compromising performance. Further analysis demonstrates that our methods are effective by maintaining reflection frequency for hard problems while appropriately reducing it for simpler ones without losing reflection ability. Codes are available at https://github.com/hexuandeng/REA-RL.

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