@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://aclanthology.org/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{\%}."
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<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%.</abstract>
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%0 Conference Proceedings
%T MemPO: Self-Memory Policy Optimization for Long-Horizon Agents
%A Li, Ruoran
%A Zhang, Xinghua
%A Yu, Haiyang
%A Duan, Shitong
%A Li, Xiang
%A Xiang, Wenxin
%A Liao, Chonghua
%A Guo, Xudong
%A Li, Yongbin
%A Suo, Jinli
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-mempo
%X 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%.
%U https://aclanthology.org/2026.findings-acl.1166/
%P 23286-23301
Markdown (Informal)
[MemPO: Self-Memory Policy Optimization for Long-Horizon Agents](https://aclanthology.org/2026.findings-acl.1166/) (Li et al., Findings 2026)
ACL
- Ruoran Li, Xinghua Zhang, Haiyang Yu, Shitong Duan, Xiang Li, Wenxin Xiang, Chonghua Liao, Xudong Guo, Yongbin Li, and Jinli Suo. 2026. MemPO: Self-Memory Policy Optimization for Long-Horizon Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 23286–23301, San Diego, California, United States. Association for Computational Linguistics.