@inproceedings{liu-etal-2026-bapo,
title = "{BAPO}: Boundary-Aware Policy Optimization for Reliable Agentic Search",
author = "Liu, Shiyu and
Yin, Yongjing and
Yan, Jianhao and
Tang, Yunbo and
Zhang, Qinggang and
Li, Bei and
Chen, Xin and
Wang, Jingang and
Cai, Xunliang and
Su, Jinsong",
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.1814/",
pages = "36408--36426",
ISBN = "979-8-89176-395-1",
abstract = "RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances accuracy with agent policies optimized via large-scale reinforcement learning, we identify a critical gap in reliability: these agents fail to recognize their reasoning boundaries and rarely admit ``I DON{'}T KNOW'' even when evidence is insufficient or reasoning reaches its limit. The lack of reliability often leads to plausible but unreliable answers, introducing significant risks in many real-world scenarios. To this end, we propose Boundary-Aware Policy Optimization (BAPO), a novel RL framework designed to cultivate reliable boundary awareness without compromising accuracy. BAPO introduces two key components: (i) a group-based boundary-aware reward that encourages an IDK response only when the reasoning reaches its limit, and (ii) an adaptive reward modulator that strategically suspends this reward during early exploration, preventing the model from exploiting IDK as a shortcut. Extensive experiments on four benchmarks demonstrate that BAPO substantially enhances the overall reliability of agentic search."
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%0 Conference Proceedings
%T BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search
%A Liu, Shiyu
%A Yin, Yongjing
%A Yan, Jianhao
%A Tang, Yunbo
%A Zhang, Qinggang
%A Li, Bei
%A Chen, Xin
%A Wang, Jingang
%A Cai, Xunliang
%A Su, Jinsong
%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 liu-etal-2026-bapo
%X RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances accuracy with agent policies optimized via large-scale reinforcement learning, we identify a critical gap in reliability: these agents fail to recognize their reasoning boundaries and rarely admit “I DON’T KNOW” even when evidence is insufficient or reasoning reaches its limit. The lack of reliability often leads to plausible but unreliable answers, introducing significant risks in many real-world scenarios. To this end, we propose Boundary-Aware Policy Optimization (BAPO), a novel RL framework designed to cultivate reliable boundary awareness without compromising accuracy. BAPO introduces two key components: (i) a group-based boundary-aware reward that encourages an IDK response only when the reasoning reaches its limit, and (ii) an adaptive reward modulator that strategically suspends this reward during early exploration, preventing the model from exploiting IDK as a shortcut. Extensive experiments on four benchmarks demonstrate that BAPO substantially enhances the overall reliability of agentic search.
%U https://aclanthology.org/2026.findings-acl.1814/
%P 36408-36426
Markdown (Informal)
[BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search](https://aclanthology.org/2026.findings-acl.1814/) (Liu et al., Findings 2026)
ACL
- Shiyu Liu, Yongjing Yin, Jianhao Yan, Yunbo Tang, Qinggang Zhang, Bei Li, Xin Chen, Jingang Wang, Xunliang Cai, and Jinsong Su. 2026. BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search. In Findings of the Association for Computational Linguistics: ACL 2026, pages 36408–36426, San Diego, California, United States. Association for Computational Linguistics.