Lifelong Learning of Large Language Model based Agents: A Roadmap

TL;DR

This paper provides a comprehensive roadmap for integrating lifelong learning into large language model-based agents, emphasizing modular design, adaptation, and long-term performance in dynamic environments.

Contribution

It systematically categorizes techniques for enabling lifelong learning in LLM agents, highlighting core modules and offering a research roadmap.

Findings

Categorization of core modules: perception, memory, action.

Identification of challenges like catastrophic forgetting.

Guidelines for evaluation and future research directions.

Abstract

Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing Artificial General Intelligence (AGI) by enabling systems to continuously adapt in dynamic environments. While large language models (LLMs) have demonstrated impressive capabilities in natural language processing, existing LLM agents are typically designed for static systems and lack the ability to adapt over time in response to new challenges. This survey is the first to systematically summarize the potential techniques for incorporating lifelong learning into LLM-based agents. We categorize the core components of these agents into three modules: the perception module for multimodal input integration, the memory module for storing and retrieving evolving knowledge, and the action module for grounded interactions with the dynamic environment. We highlight how these pillars collectively enable continuous adaptation, mitigate catastrophic forgetting, and improve long-term performance. This survey provides a roadmap for researchers and practitioners working to develop lifelong learning capabilities in LLM agents, offering insights into emerging trends, evaluation metrics, and application scenarios. Relevant literature and resources are available at \href{this url}{https://github.com/qianlima-lab/awesome-lifelong-llm-agent}.