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.
@article{arxiv.2604.16004,
title = {AgentV-RL: Scaling Reward Modeling with Agentic Verifier},
author = {Jiazheng Zhang and Ziche Fu and Zhiheng Xi and Wenqing Jing and Mingxu Chai and Wei He and Guoqiang Zhang and Chenghao Fan and Chenxin An and Wenxiang Chen and Zhicheng Liu and Haojie Pan and Dingwei Zhu and Tao Gui and Qi Zhang and Xuanjing Huang},
journal= {arXiv preprint arXiv:2604.16004},
year = {2026}
}