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[2511.07922] SERL: Self-Examining Reinforcement Learning on Open-Domain
Summary
The paper introduces Self-Examining Reinforcement Learning (SERL), a novel framework that enhances the performance of large language models (LLMs) in open-domain tasks by using self-generated rewards.
Why It Matters
SERL addresses key challenges in applying reinforcement learning to open-domain tasks, particularly the lack of verifiable rewards and reliance on external feedback. By enabling LLMs to self-assess, this approach could lead to more robust and effective AI systems, enhancing their applicability across various domains.
Key Takeaways
- SERL allows LLMs to act as both Actor and Judge, improving self-assessment.
- The framework introduces two synergistic reward mechanisms derived from self-generated comparisons.
- Experiments show SERL outperforms existing self-improvement methods, achieving state-of-the-art results.
- The method enhances the performance of smaller models to levels comparable to larger ones.
- SERL's approach could revolutionize how reinforcement learning is applied in open-domain scenarios.
Computer Science > Machine Learning arXiv:2511.07922 (cs) [Submitted on 11 Nov 2025 (v1), last revised 25 Feb 2026 (this version, v3)] Title:SERL: Self-Examining Reinforcement Learning on Open-Domain Authors:Weixuan Ou, Yanzhao Zheng, Shuoshuo Sun, Wei Zhang, Baohua Dong, Hangcheng Zhu, Ruohui Huang, Gang Yu, Pengwei Yan, Yifan Qiao View a PDF of the paper titled SERL: Self-Examining Reinforcement Learning on Open-Domain, by Weixuan Ou and 8 other authors View PDF HTML (experimental) Abstract:Reinforcement Learning (RL) has been shown to improve the capabilities of large language models (LLMs). However, applying RL to open-domain tasks faces two key challenges: (1) the inherent subjectivity of these tasks prevents the verifiable rewards as required by Reinforcement Learning with Verifiable Rewards (RLVR); (2) Reinforcement Learning from Human Feedback (RLHF) relies on external reward mechanisms. To overcome these limitations, we propose Self-Examining Reinforcement Learning (SERL), a novel self-improving framework where the LLM serves as both Actor and Judge. SERL introduces two synergistic reward mechanisms without any external signals. On the one hand, to improve the Actor's capability, we derive rewards from Copeland-style pairwise comparison judgments across a group of generated responses. On the other hand, a self-consistency reward that encourages coherent judgments is proposed to improve the Judge's reliability. This process refines the Judge's capability, which in ...
