Rewarding precision cuts padded answers and lifts HealthBench win rate to 74.1%
Rubric-based rewards let reinforcement learning train on tasks without one right answer, but they mostly reward covering more rubric points, so trained models grow verbose and add wrong content. The FORK reward also scores precision: each part of an answer must be necessary and factually correct. Against the recall-only reward it kept similar coverage with shorter answers, and its win rate over the base model rose from 22.1% to 74.1% on HealthBench.