LifelongAgentBench: Revolutionizing Continuous Learning in LLM-Based Agents

The Challenge of Lifelong Learning in LLM Agents

Lifelong learning is essential for intelligent agents operating in dynamic environments. Current large language model (LLM)-based agents, however, lack the ability to remember past experiences, treating each task as entirely new. While LLMs have revolutionized language tasks and inspired agent-based systems, these agents remain stateless and unable to adapt knowledge over time. Achieving general intelligence requires agents that can retain, adapt, and reuse knowledge continuously.

Limitations of Existing Benchmarks

Most existing benchmarks focus on isolated tasks without emphasizing knowledge reuse or retention. They typically evaluate performance on one-time tasks, ignoring the sequential nature of learning needed for lifelong adaptation. Additionally, problems like label errors and reproducibility challenges hamper the practical assessment of continuous learning capabilities.

Introducing LifelongAgentBench

Researchers from South China University of Technology, MBZUAI, Chinese Academy of Sciences, and East China Normal University have developed LifelongAgentBench — the first comprehensive benchmark tailored to evaluate lifelong learning in LLM-based agents. This benchmark features interconnected skill-driven tasks across three environments: Databases, Operating Systems, and Knowledge Graphs. It integrates label verification, reproducibility, and modular design for rigorous evaluation.

Novel Techniques to Enhance Learning

The study found that conventional experience replay methods often fail due to irrelevant information and constraints on context length. To overcome these issues, the team proposed a group self-consistency mechanism that clusters past experiences and applies voting strategies, significantly improving lifelong learning performance across various LLM architectures.

Benchmark Design and Implementation

LifelongAgentBench treats lifelong learning as a sequential decision-making problem modeled by goal-conditioned partially observable Markov decision processes (POMDPs). Tasks are designed to reflect real-world complexities, with overlapping skills and environmental noise. Both automated and manual validation processes ensure task quality and diversity.

Its modular framework includes separate components for agents, environments, and controllers that communicate via RPC, emphasizing reproducibility and flexibility. Experiments demonstrate that experience replay can boost performance, especially on complex tasks, but memory overhead remains a challenge, highlighting the need for smarter memory management.

Future Directions

LifelongAgentBench lays the groundwork for developing adaptive, memory-efficient LLM-based agents capable of continuous learning. Future work will explore more efficient memory strategies and extend evaluations to multimodal real-world tasks, pushing toward truly intelligent, lifelong learning agents.