Agent-Omit: Training Efficient LLM Agents for Adaptive Thought and Observation Omission via Agentic Reinforcement Learning

Large language model (LLM) agents, while powerful, often suffer from inefficiencies due to processing irrelevant information and generating verbose thoughts. Agent-Omit introduces a novel training paradigm that leverages agentic reinforcement learning to teach LLM agents to adaptively omit redundant thoughts and observations. This results in significantly more efficient and performant agents, reducing computational overhead and improving task completion rates in complex environments.