@inproceedings{li-etal-2026-mempo,
    title = "{M}em{PO}: Self-Memory Policy Optimization for Long-Horizon Agents",
    author = "Li, Ruoran  and
      Zhang, Xinghua  and
      Yu, Haiyang  and
      Duan, Shitong  and
      Li, Xiang  and
      Xiang, Wenxin  and
      Liao, Chonghua  and
      Guo, Xudong  and
      Li, Yongbin  and
      Suo, Jinli",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1166/",
    pages = "23286--23301",
    ISBN = "979-8-89176-395-1",
    abstract = "Long-horizon agents face the challenge of growing context size during interaction with environment, which degrades the performance and stability. Existing methods typically introduce the external memory module and look up the relevant information from the stored memory, which prevents the model itself from proactively managing its memory content and aligning with the agent{'}s overarching task objectives. To address these limitations, we propose the self-memory policy optimization algorithm (MemPO), which enables the agent (policy model) to autonomously summarize and manage their memory during interaction with environment. By improving the credit assignment mechanism based on memory effectiveness, the policy model can selectively retain crucial information, significantly reducing token consumption while preserving task performance. Extensive experiments and analyses confirm that MemPO achieves absolute F1 score gains of 25.98 over the base model and 7.1 over the previous SOTA baseline, while reducing token usage by 67.58{\%} and 73.12{\%}."
}