Imitation Learning for Multi-turn LM Agents via On-policy Expert Corrections

This article likely discusses a novel approach to training Language Model (LM) agents for multi-turn conversations. The core idea seems to be using imitation learning, where the agent learns from an expert. The 'on-policy expert corrections' suggests a method to refine the agent's behavior during the learning process, potentially improving its performance in complex, multi-turn dialogues. The focus is on improving the agent's ability to handle multi-turn interactions, which is a key challenge in building effective conversational AI.