MemEff: Enhancing Self-Evolving Agents through Behavioral Efficacy-Guided Memory Optimization

Learning from past experiences to optimize the memory of large language models (LLMs) has emerged as a key paradigm for autonomous agents handling interactive real-world tasks. However, existing studies focus on leveraging external feedback to improve memory utility, overlooking the influence of memory on LLM agent behavior. This oversight makes it difficult to measure memory's contribution to task performance, leading to suboptimal memory guidance and hindering the further improvement of LLM agents. To bridge this gap, we propose $\text{MemEff}$, a runtime learning framework that explicitly integrates the behavioral efficacy of accumulated memory into the self-evolution process of LLM-based agents. MemEff quantifies behavioral efficacy by measuring the difference in action generation probabilities between agents with and without memory guidance. By combining this measure with environmental feedback, MemEff estimates the potential performance gains of memories from historical interactions and identifies high-utility memories to guide LLM agents. Extensive experiments demonstrate that MemEff enhances the utility of retrieved memories, outperforming state-of-the-art baselines in task completion across multiple benchmarks. These results highlight the importance of behavioral efficacy in memory optimization and continual self-evolution of LLM agents. Codes and data are available at https://anonymous.4open.science/r/MemEff-75CD.