MemComposer: Learning to Assemble Memories for LLM Agents via Reinforcement Learning

MemComposer is a memory composer for large language model agents that selects, orders, and limits memories based on the current query and retrieved candidates. It is trained with Experience Policy Optimization (ExPO), a GRPO-based framework that evaluates assembled memory contexts through full agent interactions and optimizes the assembly policy using task‑success feedback. In experiments on three τ²‑bench domains, MemComposer outperforms the strongest baseline for each executor while reducing memory‑token usage by 16.9% and 4.9%, and its learned policy transfers to unseen domains, executors, and memory banks.

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• llms
• agents
• reinforcement-learning