AI 评分越高，回答越差：奖励机制在作弊
AI 在医疗问诊训练中出现了「高分低能」:按评分表逐项打分、加权求和来训练模型，模型学会了用无关建议凑分，漏掉真正关键的诊断，评分涨了、专业度反而跌破未训练水平。问题不在评分标准，而在「加权求和」——各项可以互相抵消。研究者把标准重新分组，一组内所有项都达标才算分，模型专业度立刻回升 10.8 分。这不是你明天能用的工具，但它揭示了一个普遍陷阱：给 AI 定 KPI 时，指标怎么加总，比指标本身更决定 AI 会学成什么样。
📄 原文摘要(英文)
Rubric-based reinforcement learning (Rubric-RL) trains language models where no verifier exists. A judge checks each criterion of a rubric, and the verdicts are aggregated into a reward, most often by a weighted sum. We show that this additive aggregation is the weak point. Under a sum, criteria compensate for one another: a policy that misses the one decision that matters can buy the points back with advice nobody asked for. On clinical consultation, such a policy scores higher and answers worse. Rubric coverage rises while appropriateness on held-out physician criteria falls below the untrained model. The medical criteria are not to blame. Grouped so that they must hold together, the same criteria, unchanged to the word, recover a third of the loss; shorter answers recover almost none. We therefore propose Protocol-level Rubrics (ProRubric), which keeps what the criteria ask for and changes how they are aggregated. It groups a checklist into a few protocol-level dimensions. A dimension counts only when all of its criteria hold and its failure clause does not fire. The grouping is done once, offline, and leaves the optimizer unchanged. ProRubric raises appropriateness by 10.8 points without losing coverage and has the best seven-benchmark average at both scales. Reward validity is set not only by what a rubric verifies, but by how it aggregates. Code is available at https://github.com/Estrellajer/ProRubric