SERL: Self-Examining Reinforcement Learning on Open-Domain

arXiv — cs.LG•Wednesday, November 12, 2025 at 5:00:00 AM

The introduction of Self-Examining Reinforcement Learning (SERL) marks a significant advancement in the application of reinforcement learning to open-domain tasks, which have traditionally faced challenges due to their subjective nature. By enabling large language models (LLMs) to function as both actor and judge, SERL eliminates the reliance on external reward mechanisms, thus streamlining the learning process. The framework employs two innovative reward mechanisms: one based on Copeland-style pairwise comparisons and another that promotes self-consistency in judgments. Experimental results indicate that SERL not only outperforms existing self-improvement methods but also achieves performance levels comparable to much larger models, such as Qwen3-32B. This breakthrough is crucial as it enhances the capabilities of LLMs, paving the way for more effective and autonomous AI systems in various applications.

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