ALOHA: Agentic LLM-driven Online Hyperparameter Adaption For Reinforcement Learning

Abstract

We introduce ALOHA (Agentic LLM-driven Online Hyperparameter Adaptation), a method for online hyperparameter optimization within a single training run, that embeds a large language model directly into the RL training loop to adaptively adjust hyperparameters at fixed intervention points based on online training metrics, requiring no preprocessing and no parallel agents.