@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://preview.aclanthology.org/ingest-acl/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."
}