Jong-Heon Kim

Eat Your Precision: Precision Objective Mitigates Reward Hacking in Rubric-based RL

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 al...