@inproceedings{yan-etal-2026-memory,
    title = "Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning",
    author = "Yan, Sikuan  and
      Yang, Xiufeng  and
      Huang, Zuchao  and
      Nie, Ercong  and
      Ding, Zifeng  and
      Li, Zonggen  and
      Ma, Xiaowen  and
      Bi, Jinhe  and
      Kersting, Kristian  and
      Pan, Jeff Z.  and
      Schuetze, Hinrich  and
      Tresp, Volker  and
      Ma, Yunpu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.583/",
    pages = "12805--12825",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless, constrained by limited context windows that hinder long-horizon reasoning. Recent efforts to address this limitation often augment LLMs with an external memory bank, yet most existing pipelines are static and heuristic-driven, lacking a learned mechanism for deciding what to store, update, or retrieve. We present Memory-R1, a reinforcement learning (RL) framework that equips LLMs with the ability to actively manage and utilize external memory through two specialized agents: a Memory Manager that learns structured operations, including ADD, UPDATE, DELETE, and NOOP; and an Answer Agent that pre-selects and reasons over relevant entries. Both agents are fine-tuned with outcome-driven RL (PPO and GRPO), enabling adaptive memory management with minimal supervision. With only 152 training QA pairs, Memory-R1 outperforms strong baselines and generalizes across diverse question types, three benchmarks (LoCoMo, MSC, LongMemEval), and multiple model scales (3B{--}14B)."
}