@inproceedings{chen-etal-2026-knowgpo,
title = "{K}now{GPO}: Knowledge-Grouped Policy Optimization for Search Agents",
author = "Chen, Siqi and
Lu, Zhengxi and
Yan, Yuchen and
Wu, Xingyu and
Chen, Qipeng and
Zhang, Xin and
Liu, Aiting and
Deng, Chao and
Liu, Jie and
Ma, Jin and
Lu, Weiming and
Xiao, Jun and
Shen, Yongliang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2357/",
pages = "53526--53546",
ISBN = "979-8-89176-459-0",
abstract = "Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions is fundamentally difficult. Outcome-based reinforcement learning provides only a single terminal reward shared across an entire trajectory. Recent process-supervision methods aim to assign credit at finer granularity, but they typically anchor credit to either surface-level trajectory states or model-internal signals, both of which become unreliable in deep search where observations are heterogeneous and retrieved contexts are long and noisy. We propose KnowGPO, a reinforcement learning framework that aligns trajectories by the task-relevant knowledge they have acquired rather than by their surface observations. For each query, KnowGPO decomposes the information required to derive the answer into atomic knowledge conditions, and represents the state of a trajectory at each step by the subset of conditions it has satisfied. Trajectories sharing the same knowledge state are grouped for within-group comparison, and the incremental knowledge gain between consecutive steps serves as an environment-grounded process reward. Experiments on Xbench, BrowseComp-ZH, and GAIA show KnowGPO consistently outperforms outcome-only (GRPO) and process-supervision baselines (GiGPO, IGPO) by 3.8 to 4.2 points, with our 8B model rivaling or exceeding most 32B open-source systems. Our code is available at https://github.com/ZJU-REAL/KnowGPO."
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<abstract>Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions is fundamentally difficult. Outcome-based reinforcement learning provides only a single terminal reward shared across an entire trajectory. Recent process-supervision methods aim to assign credit at finer granularity, but they typically anchor credit to either surface-level trajectory states or model-internal signals, both of which become unreliable in deep search where observations are heterogeneous and retrieved contexts are long and noisy. We propose KnowGPO, a reinforcement learning framework that aligns trajectories by the task-relevant knowledge they have acquired rather than by their surface observations. For each query, KnowGPO decomposes the information required to derive the answer into atomic knowledge conditions, and represents the state of a trajectory at each step by the subset of conditions it has satisfied. Trajectories sharing the same knowledge state are grouped for within-group comparison, and the incremental knowledge gain between consecutive steps serves as an environment-grounded process reward. Experiments on Xbench, BrowseComp-ZH, and GAIA show KnowGPO consistently outperforms outcome-only (GRPO) and process-supervision baselines (GiGPO, IGPO) by 3.8 to 4.2 points, with our 8B model rivaling or exceeding most 32B open-source systems. Our code is available at https://github.com/ZJU-REAL/KnowGPO.</abstract>
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%0 Conference Proceedings
%T KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents
%A Chen, Siqi
%A Lu, Zhengxi
%A Yan, Yuchen
%A Wu, Xingyu
%A Chen, Qipeng
%A Zhang, Xin
%A Liu, Aiting
%A Deng, Chao
%A Liu, Jie
%A Ma, Jin
%A Lu, Weiming
%A Xiao, Jun
%A Shen, Yongliang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chen-etal-2026-knowgpo
%X Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions is fundamentally difficult. Outcome-based reinforcement learning provides only a single terminal reward shared across an entire trajectory. Recent process-supervision methods aim to assign credit at finer granularity, but they typically anchor credit to either surface-level trajectory states or model-internal signals, both of which become unreliable in deep search where observations are heterogeneous and retrieved contexts are long and noisy. We propose KnowGPO, a reinforcement learning framework that aligns trajectories by the task-relevant knowledge they have acquired rather than by their surface observations. For each query, KnowGPO decomposes the information required to derive the answer into atomic knowledge conditions, and represents the state of a trajectory at each step by the subset of conditions it has satisfied. Trajectories sharing the same knowledge state are grouped for within-group comparison, and the incremental knowledge gain between consecutive steps serves as an environment-grounded process reward. Experiments on Xbench, BrowseComp-ZH, and GAIA show KnowGPO consistently outperforms outcome-only (GRPO) and process-supervision baselines (GiGPO, IGPO) by 3.8 to 4.2 points, with our 8B model rivaling or exceeding most 32B open-source systems. Our code is available at https://github.com/ZJU-REAL/KnowGPO.
%U https://aclanthology.org/2026.emnlp-main.2357/
%P 53526-53546
Markdown (Informal)
[KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents](https://aclanthology.org/2026.emnlp-main.2357/) (Chen et al., EMNLP 2026)
ACL
- Siqi Chen, Zhengxi Lu, Yuchen Yan, Xingyu Wu, Qipeng Chen, Xin Zhang, Aiting Liu, Chao Deng, Jie Liu, Jin Ma, Weiming Lu, Jun Xiao, and Yongliang Shen. 2026. KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 53526–53546, Budapest, Hungary. Association for Computational Linguistics.