arXiv Computation and Language October 22, 2026 By Afonso Baldo, Ant\'onio Farinhas, Marcos Treviso, Ricardo Rei, Nuno M. Guerreiro

Mitigating Reward Hacking in Preference Reward Models with Evolving Penalties

Read the original on arXiv Computation and Language →

The paper introduces Dynalize, an online penalty system that mitigates reward hacking in preference reward models during policy training. By having a language model periodically analyze rollouts and generate penalties for behaviors the reward model inadvertently rewards, Dynalize prevents the policy from inflating scores without improving quality. Experiments in clinical counseling and creative writing show that Dynalize sustains longer training and yields better downstream performance, suppressing undesirable patterns even in multi‑turn settings and after retraining the reward model.

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