ReSum: Unlocking Long-Horizon Search Intelligence via Context
  Summarization
Large Language Model (LLM)-based web agents demonstrate strong performance on
knowledge-intensive tasks but are hindered by context window limitations in
paradigms like ReAct. Complex queries involving multiple entities, intertwined
relationships, and high uncertainty demand extensive search cycles that rapidly
exhaust context budgets before reaching complete solutions. To overcome this
challenge, we introduce ReSum, a novel paradigm that enables indefinite
exploration through periodic context summarization. ReSum converts growing
interaction histories into compact reasoning states, maintaining awareness of
prior discoveries while bypassing context constraints. For paradigm adaptation,
we propose ReSum-GRPO, integrating GRPO with segmented trajectory training and
advantage broadcasting to familiarize agents with summary-conditioned
reasoning. Extensive experiments on web agents of varying scales across three
benchmarks demonstrate that ReSum delivers an average absolute improvement of
4.5\% over ReAct, with further gains of up to 8.2\% following ReSum-GRPO
training. Notably, with only 1K training samples, our WebResummer-30B (a
ReSum-GRPO-trained version of WebSailor-30B) achieves 33.3\% Pass@1 on
BrowseComp-zh and 18.3\% on BrowseComp-en, surpassing existing open-source web
agents.
68ca3c926e0073c09bd1df32
Xixi Wu
claimed_verified
2025-09-17T12:47:44.759Z
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Xixi Wu
user
xxwu
68ca3c926e0073c09bd1df33
Kuan Li
68ca3c926e0073c09bd1df34
Yida Zhao
claimed_verified
2025-09-17T15:29:22.732Z
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Yida Zhao
user
zhaoyd
68ca3c926e0073c09bd1df35
Liwen Zhang
68ca3c926e0073c09bd1df36
Litu Ou
admin_assigned
2025-09-19T06:56:00.748Z
622f2feea32d46b4be9ed8c4
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/NDeZQZQK5U-9m10yQwDVf.png
Litu Ou
user
learn3r
68ca3c926e0073c09bd1df37
Huifeng Yin
68ca3c926e0073c09bd1df38
Zhongwang Zhang
admin_assigned
2025-09-19T06:56:19.806Z
657c4e471fa4e4e152d31b40
/avatars/0a375c40b7eb75987f4aed958f55c967.svg
zhongwang zhang
user
0123zzw666
68ca3c926e0073c09bd1df39
Yong Jiang
68ca3c926e0073c09bd1df3a
Pengjun Xie
admin_assigned
2025-09-19T06:56:31.306Z
63a091e42fabbbb89991f5ce
/avatars/d55485b06461764c36c9edf9d6e8892c.svg
pengjun xie
user
xpjandy
68ca3c926e0073c09bd1df3b
Fei Huang
68ca3c926e0073c09bd1df3c
Minhao Cheng
admin_assigned
2025-09-19T06:56:42.102Z
67cf1d6310dd1d670adf74ce
/avatars/15b3bef32acdc37093e2fc0b32a7b827.svg
Minhao Cheng
user
cmhcbb
68ca3c926e0073c09bd1df3d
Shuai Wang
68ca3c926e0073c09bd1df3e
Hong Cheng
68ca3c926e0073c09bd1df3f
Jingren Zhou
admin_assigned
2025-09-19T06:55:44.622Z
602f88f5e8149a962412a667
/avatars/b78f0e583df8e5d5e3365934fe5f4900.svg
Zhou
user
Jingren
ReSum: Unlocking Long-Horizon Search Intelligence via Context
  Summarization
Large Language Model (LLM)-based web agents demonstrate strong performance on
knowledge-intensive tasks but are hindered by context window limitations in
paradigms like ReAct. Complex queries involving multiple entities, intertwined
relationships, and high uncertainty demand extensive search cycles that rapidly
exhaust context budgets before reaching complete solutions. To overcome this
challenge, we introduce ReSum, a novel paradigm that enables indefinite
exploration through periodic context summarization. ReSum converts growing
interaction histories into compact reasoning states, maintaining awareness of
prior discoveries while bypassing context constraints. For paradigm adaptation,
we propose ReSum-GRPO, integrating GRPO with segmented trajectory training and
advantage broadcasting to familiarize agents with summary-conditioned
reasoning. Extensive experiments on web agents of varying scales across three
benchmarks demonstrate that ReSum delivers an average absolute improvement of
4.5\% over ReAct, with further gains of up to 8.2\% following ReSum-GRPO
training. Notably, with only 1K training samples, our WebResummer-30B (a
ReSum-GRPO-trained version of WebSailor-30B) achieves 33.3\% Pass@1 on
BrowseComp-zh and 18.3\% on BrowseComp-en, surpassing existing open-source web
agents.
ReSum, a novel paradigm with periodic context summarization, enhances web agents' performance on knowledge-intensive tasks by overcoming context window limitations, achieving significant improvements over ReAct.
Large Language Model LLM web agents knowledge-intensive tasks context window limitations ReAct ReSum context summarization reasoning states GRPO segmented trajectory training advantage broadcasting ReSum-GRPO WebResummer-30B WebSailor-30B BrowseComp-zh BrowseComp-en Pass@1