MemRetriever: Learning to Search, Reflect, and Retrieve from Long-Term Memory

Abstract

Long-term memory enables personalized agents, but its value depends on retrieving the right evidence at the right time. Most memory systems use static top-k retrieval: they issue one query, return a fixed number of memories, and pass them directly to a downstream model. This approach can miss evidence distributed across sessions, introduce irrelevant content, and waste context, especially for multi-hop, temporal, and knowledge-update questions. We present MemRetriever, an agentic retrieval model that treats memory access as a multi-step search process. At each step, MemRetriever reasons over the current evidence and selects parallel search for broad exploration, serial search for targeted completion, or reflection and denoising for filtering and evidence assessment. It stops when the retained evidence is sufficient for downstream answering. We construct ReAct-style search-memory trajectories for supervised warm-start training and further optimize the model with Group Relative Policy Optimization. The reward design encourages evidence coverage, noise reduction, answer sufficiency, and efficient termination. Experiments on LOCOMO, LongMemEval, HotpotQA, MuSiQue, and 2WikiMultiHopQA show consistent improvements over static retrieval and supervised-only baselines. MemRetriever-4B-RL also outperforms DeepSeek-v4-Flash on the main LongMemEval retrieval metrics under the same pipeline and achieves the strongest results among the compared methods on MuSiQue. Because its decision logic is independent of the storage backend, MemRetriever can also operate over external knowledge bases and vector databases. These results show that an intermediate decision layer that plans, searches, filters evidence, and determines when to stop can improve both long-term memory retrieval and knowledge-intensive question answering.