@inproceedings{sun-etal-2026-autosearch,
title = "{A}uto{S}earch: Adaptive Search Depth for Efficient Agentic {RAG} via Reinforcement Learning",
author = "Sun, Jingbo and
Chong, Wenyue and
Tu, Songjun and
Zhang, Qichao and
Zhang, Yaocheng and
Chai, Jiajun and
Wang, Xiaohan and
Lin, Wei and
Yin, Guojun and
Zhao, Dongbin",
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.1399/",
pages = "28059--28079",
ISBN = "979-8-89176-395-1",
abstract = "Agentic retrieval-augmented generation (RAG) systems enable large language models (LLMs) to solve complex tasks through multi-step interaction with external retrieval tools. However, such multi-step interaction often involves redundant search steps, incurring substantial computational cost and latency. Prior work limits search depth (i.e., the number of search steps) to reduce cost, but this often leads to underexploration of complex questions. To address this, we first investigate how search depth affects accuracy and find a minimal sufficient search depth that defines an accuracy-efficiency trade-off, jointly determined by question complexity and the agent{'}s capability. Furthermore, we propose AutoSearch, a reinforcement learning framework that evaluates each search step via self-generated intermediate answers. By a self-answering mechanism, AutoSearch identifies the minimal sufficient search depth and promotes efficient search by rewarding its attainment while penalizing over-searching. In addition, reward mechanisms are introduced to stabilize search behavior and improve answer quality on complex questions. Extensive experiments on multiple benchmarks show that AutoSearch achieves a superior accuracy-efficiency trade-off, alleviating over-searching while preserving search quality."
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%0 Conference Proceedings
%T AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning
%A Sun, Jingbo
%A Chong, Wenyue
%A Tu, Songjun
%A Zhang, Qichao
%A Zhang, Yaocheng
%A Chai, Jiajun
%A Wang, Xiaohan
%A Lin, Wei
%A Yin, Guojun
%A Zhao, Dongbin
%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 sun-etal-2026-autosearch
%X Agentic retrieval-augmented generation (RAG) systems enable large language models (LLMs) to solve complex tasks through multi-step interaction with external retrieval tools. However, such multi-step interaction often involves redundant search steps, incurring substantial computational cost and latency. Prior work limits search depth (i.e., the number of search steps) to reduce cost, but this often leads to underexploration of complex questions. To address this, we first investigate how search depth affects accuracy and find a minimal sufficient search depth that defines an accuracy-efficiency trade-off, jointly determined by question complexity and the agent’s capability. Furthermore, we propose AutoSearch, a reinforcement learning framework that evaluates each search step via self-generated intermediate answers. By a self-answering mechanism, AutoSearch identifies the minimal sufficient search depth and promotes efficient search by rewarding its attainment while penalizing over-searching. In addition, reward mechanisms are introduced to stabilize search behavior and improve answer quality on complex questions. Extensive experiments on multiple benchmarks show that AutoSearch achieves a superior accuracy-efficiency trade-off, alleviating over-searching while preserving search quality.
%U https://aclanthology.org/2026.findings-acl.1399/
%P 28059-28079
Markdown (Informal)
[AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning](https://aclanthology.org/2026.findings-acl.1399/) (Sun et al., Findings 2026)
ACL
- Jingbo Sun, Wenyue Chong, Songjun Tu, Qichao Zhang, Yaocheng Zhang, Jiajun Chai, Xiaohan Wang, Wei Lin, Guojun Yin, and Dongbin Zhao. 2026. AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 28059–28079, San Diego, California, United States. Association for Computational Linguistics.