MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning

ArXi:2511.02805v2 Announce Type: replace-cross LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs, and increasing compute cost and GPU memory overhead. To address this issue, we propose MemSearcher, an agent framework that maintains a compact memory during multi-turn interactions, retaining only question-relevant information and thereby keeping the context length stable across turns