arXiv AI October 1, 2026 By Maoqi Liu, Junwei He, Bowen Zhang, Feiran Li, Wentao Ma, Rongyi Lin, Shuhan Zhong, Quan Fang

Scoring Higher, Answering Worse: Mitigating Reward Hacking in Rubric-Based RL via Protocol-Level Rubrics

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The paper identifies a flaw in rubric‑based reinforcement learning where additive reward aggregation allows policies to compensate for missing critical criteria, leading to higher scores but poorer answers, especially in clinical consultation tasks. It demonstrates that grouping rubric criteria into protocol‑level dimensions—so a dimension only counts when all its criteria are satisfied—mitigates this reward hacking. The proposed Protocol‑level Rubrics (ProRubric) improve appropriateness by 10.8 points without sacrificing coverage and achieve the best performance across seven benchmarks.

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