ReSum: Unlocking Long-Horizon Search Intelligence via Context
  Summarization
Authors: name: Xixi Wu, name: Kuan Li, name: Yida Zhao, name: Liwen Zhang, name: Litu Ou, name: Huifeng Yin, name: Zhongwang Zhang, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Minhao Cheng, name: Shuai Wang, name: Hong Cheng, name: Jingren Zhou

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.