Rubric Dropout: A One-Line Fix to Mitigate Reward Hacking in RL

burny_tech · x · 2026-08-14

Introduces the paper "Rubric Dropout," which aims to mitigate the prevalent reward hacking problem in reinforcement learning with LLMs.

• Background: When using LLM judges as rubric-based rewards for RL, policies eventually learn to exploit the rubric. Experiments reveal that while the training judge's score keeps climbing, a stronger gold judge's score peaks and then declines.
• Method: Borrowing the concept of neuron dropout, the authors propose Rubric Dropout. It randomly drops a subset of rubric criteria before computing the reward at each step, ensuring the policy never optimizes the exact same rubric twice.
• Results: This simple, one-line fix effectively prevents models from overfitting to fixed judging criteria, thereby improving generalization.

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