@inproceedings{yuan-etal-2026-memsearcher,
title = "{M}em{S}earcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning",
author = "Yuan, Qianhao and
Lou, Jie and
Li, Zichao and
Chen, Jiawei and
Lu, Yaojie and
Lin, Hongyu and
Sun, Le and
Zhang, Debing and
Han, Xianpei",
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.736/",
pages = "14965--14977",
ISBN = "979-8-89176-395-1",
abstract = "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. Training MemSearcher is challenging because each trajectory spans multiple turns under different LLM contexts, making each turn an independent optimization target in reinforcement learning. We introduce multi-context GRPO, which propagates trajectory-level advantages to all turns for end-to-end optimization. Experiments demonstrate that MemSearcher outperforms strong history-concatenation (ReAct-style) baselines on a range of public datasets while maintaining nearly constant token counts across multi-turn interactions. The code and models will be publicly available at https://github.com/icip-cas/MemSearcher."
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<abstract>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. Training MemSearcher is challenging because each trajectory spans multiple turns under different LLM contexts, making each turn an independent optimization target in reinforcement learning. We introduce multi-context GRPO, which propagates trajectory-level advantages to all turns for end-to-end optimization. Experiments demonstrate that MemSearcher outperforms strong history-concatenation (ReAct-style) baselines on a range of public datasets while maintaining nearly constant token counts across multi-turn interactions. The code and models will be publicly available at https://github.com/icip-cas/MemSearcher.</abstract>
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%0 Conference Proceedings
%T MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning
%A Yuan, Qianhao
%A Lou, Jie
%A Li, Zichao
%A Chen, Jiawei
%A Lu, Yaojie
%A Lin, Hongyu
%A Sun, Le
%A Zhang, Debing
%A Han, Xianpei
%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 yuan-etal-2026-memsearcher
%X 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. Training MemSearcher is challenging because each trajectory spans multiple turns under different LLM contexts, making each turn an independent optimization target in reinforcement learning. We introduce multi-context GRPO, which propagates trajectory-level advantages to all turns for end-to-end optimization. Experiments demonstrate that MemSearcher outperforms strong history-concatenation (ReAct-style) baselines on a range of public datasets while maintaining nearly constant token counts across multi-turn interactions. The code and models will be publicly available at https://github.com/icip-cas/MemSearcher.
%U https://aclanthology.org/2026.findings-acl.736/
%P 14965-14977
Markdown (Informal)
[MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning](https://aclanthology.org/2026.findings-acl.736/) (Yuan et al., Findings 2026)
ACL
- Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen, Yaojie Lu, Hongyu Lin, Le Sun, Debing Zhang, and Xianpei Han. 2026. MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 14965–14977, San Diego, California, United States. Association for Computational Linguistics.