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

arXiv:2404.19409v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.cl hyperparameter language language models large language large language models llm llms optimization policy regularization reinforcement reinforcement learning type while