Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning

Understand

While Reinforcement Learning (RL) has been proven essential for tuning large language models (LLMs), it can lead to reward over-optimization (ROO).

• Existing approaches address ROO by adding KL regularization, requiring computationally expensive hyperparameter tuning.
• Additionally, KL regularization focuses solely on regularizing the language policy, neglecting a potential source of regularization: the reward function itself.
• Inspired by demonstration-guided RL, we here introduce the Reward Calibration from Demonstration (RCfD), which leverages human demonstrations and a reward model to recalibrate the reward objective.