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arXiv · 2509.25534

Self-Rewarding Rubric-Based Reinforcement Learning for Open-Ended Reasoning

Zhiling Ye·Yun Yue·Haowen Wang·Xudong Han·Jiadi Jiang·Cheng Wei·Lei Fan·Jiaxin Liang·Shuowen Zhang·Ji Li·Chunxiao Guo·Jian Wang·Peng Wei·Jinjie Gu
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Abstract

Open-ended evaluation is essential for deploying large language models in real-world settings. In studying HealthBench, we observe that using the model itself as a grader and generating rubric-based reward signals substantially improves reasoning performance. Remarkably, the trained model also becomes a stronger grader. Motivated by this, we introduce Self-Rewarding Rubric-Based Reinforcement Learning for Open-Ended Reasoning, a lightweight framework that enables faster and more resource-efficient training while surpassing baselines. Remarkably, on Qwen3-32B, training with just the 4000-sample HealthBench Easy subset is sufficient to obtain a model that exceeds GPT-5 on HealthBench Hard. Incorporating a small amount of teacher-graded data further enhances performance for less capable models.

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Zhiling Ye, Yun Yue, Haowen Wang, Xudong Han, Jiadi Jiang, Cheng Wei, Lei Fan, Jiaxin Liang, Shuowen Zhang, Ji Li, Chunxiao Guo, Jian Wang, Peng Wei, Jinjie Gu. 2025-09-19. Self-Rewarding Rubric-Based Reinforcement Learning for Open-Ended Reasoning. https://arxiv.org/abs/2509.25534

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