Learning to Retrieve Long-term Memories with Unlabeled Conversations

Long-term memory in LM agents relies on retrieving relevant information from external memory stores. Despite training on massive query-document pairs, general-purpose embedding models struggle with long-term memory retrieval, as query-memory relevance labels are rare and costly to obtain. In this work, we propose adding self-supervised retriever learning on unlabeled conversations to the training recipe to fill this gap. Specifically, we introduce MemRevela, which learns retrieval directly from the dependencies between conversational turns. An auxiliary language model predicts conversational fragments using retriever-weighted context, allowing the prediction loss to train the retriever. MemRevela then jointly optimizes this objective with standard MS MARCO contrastive learning. We evaluate five model sizes from 0.1B to 8B on LMEB's 22 datasets across four memory types. Without any task instructions, MemRevela-8B achieves the best performance in our comparison with dataset-averaged nDCG@10 63.24, exceeding NV-Embed-v2 by 3.43 absolute points. Our method also improves downstream question answering over long conversations compared with two proprietary embedding APIs. Without annotated labels, MemRevela injects long-term memory knowledge into the retriever: it spreads conversational turns in embedding space about as widely as general passages, whereas general-purpose models cluster them tightly. We show that conversational self-supervision and contrastive learning are complementary, each strengthening different memory types. Finally, the memory gains do not come at the expense of general retrieval: MemRevela stays comparable to contrastive-only baselines on BEIR, making conversational self-supervision a promising addition to standard retriever training.