A new arXiv paper introduces MemComposer, a method for training LLM agents to assemble memories via reinforcement learning. The authors frame agent memory as a way to reuse past experiences without changing model parameters, but they argue that simply retrieving memories by semantic similarity is not enough.
The paper's central claim is that downstream performance depends on how memories are assembled, not just on what they contain. MemComposer therefore learns a memory-assembly policy through reinforcement learning, rather than relying on fixed similarity-based retrieval.
Because only the abstract was available for this digest, details on the experimental setup and results are not covered here. The contribution, as stated, is a learning-based alternative to static memory selection for LLM agents.