Empowering Large Language Model Agents through Action Learning

arXiv:2402.15809v1 Announce Type: cross
Abstract: Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error, a key element of intelligent behavior. In this work, we argue that the capacity to learn new actions from experience is fundamental to the advancement of learning in LLM agents. While humans naturally expand their action spaces and develop skills through experiential learning, LLM agents typically operate within fixed action spaces, limiting …

abstract agents arxiv behavior capacity cs.ai cs.cl element error experience intelligent key language language model large language large language model learn llm through type work