Language Model Agent

Language model agents (LMAs) are computational systems that use large language models to perform tasks in interactive environments, aiming to bridge the gap between natural language understanding and real-world action. Current research focuses on improving LMA performance through techniques like hierarchical reinforcement learning, refined state representation optimization (e.g., selective perception), and the integration of symbolic reasoning to mitigate inherent LLM limitations such as hallucinations. These advancements hold significant potential for automating complex tasks across diverse domains, from robotic control and web automation to software engineering and even scientific writing, ultimately impacting both research methodologies and practical applications.