Rubrics Beyond Evaluation: Exploiting Rubric Structure in Reinforcement Learning
Rubrics are widely used to evaluate open-ended question answering by breaking overall quality into explicit criteria. These explicit criteria make rubrics a rich source of learning guidance, yet their potential for guiding the training process has been largely overlooked. Existing methods use rubrics mainly as direct feedback signals, without explicitly modeling the capabilities behind their criteria. This leaves their capability structure largely unused in guiding the training process. We introduce Rubric-Structured Reinforcement Learning (RSRL), an RL framework that unlocks the full potential of rubrics by exploiting their structure throughout the training process, rather than treating ...
| Authors | Yuxun Huang, Yunyao Zhang, ZeLiang Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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