AI 答得又长又错？新奖励机制专治废话
现在的 AI 在按评分标准训练时，只被鼓励「把点都答到」，于是它学会了堆内容：话更多、更啰嗦，还夹带错误，结果用户反而更不喜欢它的回答。这篇提出一个叫 FORK 的奖励机制，在「答全」之外再加一条「答准」:每句话要么是必要的、要么是事实正确的，否则不给分。效果很直接：在医疗问答上，AI 回答的胜率从 10.6% 提到 68%，长度还变短了。它不是你明天能直接用的功能，但它解释了为什么 AI 有时越训练越啰嗦——以及怎么治。
📄 原文摘要(英文)
Rubric-based rewards enable effective reinforcement learning on non-verifiable tasks. However, because these rewards are typically defined as a weighted measure of rubric coverage, models are trained primarily to maximize a recall objective. As a result, RL-trained models produce responses that are more complete but also more verbose and contain more unnecessary or factually incorrect content, leading them to be less preferred than responses from the base model. To address this issue, we present the FORK reward, which jointly optimizes recall and a novel precision objective, which requires each unit of a model response to be both necessary and factually correct, as measured by rubric-driven necessity credits and a factual-correctness grader, respectively. Our experimental results show that FORK outperforms existing rubric-based RL methods and achieves Pareto-optimal performance that balances rubric coverage and overall response quality. Compared to the original recall-based reward, FORK substantially improves precision and reduces response length while achieving similar rubric coverage, which leads to significantly higher win rate compared to the base policy (e.g., from 10.6% to 68.0% on RubricHub-Medical and from 22.1% to 74.1% on HealthBench).