@inproceedings{luo-etal-2026-reinforcing,
title = "Reinforcing Agentic Search Via Reward Density Optimization",
author = "Luo, Kun and
Qian, Hongjin and
Liu, Zheng and
Xia, Ziyi and
Xiao, Shitao and
Cao, Zhao and
Bao, Siqi and
Zhao, Jun and
Liu, Kang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.467/",
pages = "10261--10283",
ISBN = "979-8-89176-390-6",
abstract = "Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic search. However, its performance is often hindered by reward sparsity, whereby agents receive very limited positive feedback despite incurring significant exploration costs. In this paper, we formalize this challenge as a new research problem termed **Reward Density Optimization**, which aims to improve the reward obtained per unit of exploration cost. To address this problem, we introduce InfoFlow, a systematic framework that operates along three complementary dimensions: 1) **Sub-goal Scaffolding**: which decomposes long-horizon tasks into intermediate objectives and assigns process-level rewards to provide denser learning signals; 2) **Pathfinding Hints**: which injects corrective guidance into stalled trajectories to increase the ratio of successful trials; and 3) **Dual-agent Refinement**: which employs a dual-agent architecture to offload the cognitive burden of deep exploration. We evaluate InfoFlow on several popular agentic search benchmarks, where it significantly outperforms strong baselines and enables lightweight LLMs to achieve performance comparable to that of advanced proprietary models."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="luo-etal-2026-reinforcing">
<titleInfo>
<title>Reinforcing Agentic Search Via Reward Density Optimization</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kun</namePart>
<namePart type="family">Luo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hongjin</namePart>
<namePart type="family">Qian</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zheng</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ziyi</namePart>
<namePart type="family">Xia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shitao</namePart>
<namePart type="family">Xiao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhao</namePart>
<namePart type="family">Cao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Siqi</namePart>
<namePart type="family">Bao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jun</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kang</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-390-6</identifier>
</relatedItem>
<abstract>Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic search. However, its performance is often hindered by reward sparsity, whereby agents receive very limited positive feedback despite incurring significant exploration costs. In this paper, we formalize this challenge as a new research problem termed **Reward Density Optimization**, which aims to improve the reward obtained per unit of exploration cost. To address this problem, we introduce InfoFlow, a systematic framework that operates along three complementary dimensions: 1) **Sub-goal Scaffolding**: which decomposes long-horizon tasks into intermediate objectives and assigns process-level rewards to provide denser learning signals; 2) **Pathfinding Hints**: which injects corrective guidance into stalled trajectories to increase the ratio of successful trials; and 3) **Dual-agent Refinement**: which employs a dual-agent architecture to offload the cognitive burden of deep exploration. We evaluate InfoFlow on several popular agentic search benchmarks, where it significantly outperforms strong baselines and enables lightweight LLMs to achieve performance comparable to that of advanced proprietary models.</abstract>
<identifier type="citekey">luo-etal-2026-reinforcing</identifier>
<location>
<url>https://aclanthology.org/2026.acl-long.467/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>10261</start>
<end>10283</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Reinforcing Agentic Search Via Reward Density Optimization
%A Luo, Kun
%A Qian, Hongjin
%A Liu, Zheng
%A Xia, Ziyi
%A Xiao, Shitao
%A Cao, Zhao
%A Bao, Siqi
%A Zhao, Jun
%A Liu, Kang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F luo-etal-2026-reinforcing
%X Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic search. However, its performance is often hindered by reward sparsity, whereby agents receive very limited positive feedback despite incurring significant exploration costs. In this paper, we formalize this challenge as a new research problem termed **Reward Density Optimization**, which aims to improve the reward obtained per unit of exploration cost. To address this problem, we introduce InfoFlow, a systematic framework that operates along three complementary dimensions: 1) **Sub-goal Scaffolding**: which decomposes long-horizon tasks into intermediate objectives and assigns process-level rewards to provide denser learning signals; 2) **Pathfinding Hints**: which injects corrective guidance into stalled trajectories to increase the ratio of successful trials; and 3) **Dual-agent Refinement**: which employs a dual-agent architecture to offload the cognitive burden of deep exploration. We evaluate InfoFlow on several popular agentic search benchmarks, where it significantly outperforms strong baselines and enables lightweight LLMs to achieve performance comparable to that of advanced proprietary models.
%U https://aclanthology.org/2026.acl-long.467/
%P 10261-10283
Markdown (Informal)
[Reinforcing Agentic Search Via Reward Density Optimization](https://aclanthology.org/2026.acl-long.467/) (Luo et al., ACL 2026)
ACL
- Kun Luo, Hongjin Qian, Zheng Liu, Ziyi Xia, Shitao Xiao, Zhao Cao, Siqi Bao, Jun Zhao, and Kang Liu. 2026. Reinforcing Agentic Search Via Reward Density Optimization. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10261–10283, San Diego, California, United States. Association for Computational Linguistics.