@inproceedings{zhang-etal-2026-agentv,
title = "{A}gent{V}-{RL}: Scaling Reward Modeling with Agentic Verifier",
author = "Zhang, Jiazheng and
Fu, Ziche and
Xi, Zhiheng and
Jing, Wenqing and
Chai, Mingxu and
He, Wei and
Zhang, Guoqiang and
Fan, Chenghao and
An, Chenxin and
Chen, Wenxiang and
Liu, Zhicheng and
Pan, Haojie and
Zhu, Dingwei and
Gui, Tao and
Zhang, Qi and
Huang, Xuanjing",
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.1156/",
pages = "23078--23100",
ISBN = "979-8-89176-395-1",
abstract = "Verifiers have been demonstrated to enhance LLM reasoning via test-time scaling (TTS). Yet, they face significant challenges in complex domains. Error propagation from incorrect intermediate reasoning can lead to false positives for seemingly plausible solutions, while lacking external grounding makes verifiers unreliable on computation or knowledge-intensive tasks. To address these challenges, we propose Agentic Verifier, a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process. We introduce complementary forward and backward agents: one traces solutions from premises to conclusions, while the other re-checks conclusions against their underlying premises. This bidirectional process enables a comprehensive, reliable, and interpretable assessment of solutions. To facilitate practical deployment, we propose AgentV-RL. Through proactive exploration and reinforcement learning, the verifier autonomously interleaves tool-use with internal reasoning. Extensive experiments show that Agentic Verifier yields consistent performance gains under both parallel and sequential TTS. Notably, our 4B variant surpasses state-of-the-art ORMs by 25.2{\%}, positioning it as a promising paradigm for agentic reward modeling."
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<abstract>Verifiers have been demonstrated to enhance LLM reasoning via test-time scaling (TTS). Yet, they face significant challenges in complex domains. Error propagation from incorrect intermediate reasoning can lead to false positives for seemingly plausible solutions, while lacking external grounding makes verifiers unreliable on computation or knowledge-intensive tasks. To address these challenges, we propose Agentic Verifier, a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process. We introduce complementary forward and backward agents: one traces solutions from premises to conclusions, while the other re-checks conclusions against their underlying premises. This bidirectional process enables a comprehensive, reliable, and interpretable assessment of solutions. To facilitate practical deployment, we propose AgentV-RL. Through proactive exploration and reinforcement learning, the verifier autonomously interleaves tool-use with internal reasoning. Extensive experiments show that Agentic Verifier yields consistent performance gains under both parallel and sequential TTS. Notably, our 4B variant surpasses state-of-the-art ORMs by 25.2%, positioning it as a promising paradigm for agentic reward modeling.</abstract>
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%0 Conference Proceedings
%T AgentV-RL: Scaling Reward Modeling with Agentic Verifier
%A Zhang, Jiazheng
%A Fu, Ziche
%A Xi, Zhiheng
%A Jing, Wenqing
%A Chai, Mingxu
%A He, Wei
%A Zhang, Guoqiang
%A Fan, Chenghao
%A An, Chenxin
%A Chen, Wenxiang
%A Liu, Zhicheng
%A Pan, Haojie
%A Zhu, Dingwei
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%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 zhang-etal-2026-agentv
%X Verifiers have been demonstrated to enhance LLM reasoning via test-time scaling (TTS). Yet, they face significant challenges in complex domains. Error propagation from incorrect intermediate reasoning can lead to false positives for seemingly plausible solutions, while lacking external grounding makes verifiers unreliable on computation or knowledge-intensive tasks. To address these challenges, we propose Agentic Verifier, a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process. We introduce complementary forward and backward agents: one traces solutions from premises to conclusions, while the other re-checks conclusions against their underlying premises. This bidirectional process enables a comprehensive, reliable, and interpretable assessment of solutions. To facilitate practical deployment, we propose AgentV-RL. Through proactive exploration and reinforcement learning, the verifier autonomously interleaves tool-use with internal reasoning. Extensive experiments show that Agentic Verifier yields consistent performance gains under both parallel and sequential TTS. Notably, our 4B variant surpasses state-of-the-art ORMs by 25.2%, positioning it as a promising paradigm for agentic reward modeling.
%U https://aclanthology.org/2026.findings-acl.1156/
%P 23078-23100
Markdown (Informal)
[AgentV-RL: Scaling Reward Modeling with Agentic Verifier](https://aclanthology.org/2026.findings-acl.1156/) (Zhang et al., Findings 2026)
ACL
- Jiazheng Zhang, Ziche Fu, Zhiheng Xi, Wenqing Jing, Mingxu Chai, Wei He, Guoqiang Zhang, Chenghao Fan, Chenxin An, Wenxiang Chen, Zhicheng Liu, Haojie Pan, Dingwei Zhu, Tao Gui, Qi Zhang, and Xuanjing Huang. 2026. AgentV-RL: Scaling Reward Modeling with Agentic Verifier. In Findings of the Association for Computational Linguistics: ACL 2026, pages 23078–23100, San Diego, California, United States. Association for Computational Linguistics.