AgentV-RL: Scaling Reward Modeling with Agentic Verifier

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Fudan University researchers introduce Agentic Verifier, a framework designed to enhance Large Language Model (LLM) reasoning by transforming reward modeling into a multi-turn, tool-augmented deliberative process. This framework addresses challenges like error propagation and lack of external grounding in complex domains. It employs complementary forward and backward agents: the forward agent traces solutions from premises to conclusions, while the backward agent re-checks conclusions against premises, enabling comprehensive and interpretable solution assessment. To facilitate practical deployment, the team developed AgentV-RL, which uses proactive exploration and reinforcement learning to allow the verifier to autonomously interleave tool-use with internal reasoning. Experiments show Agentic Verifier yields consistent performance gains under both parallel and sequential Test-Time Scaling (TTS), with its 4B variant surpassing state-of-the-art Outcome-level Reward Models (ORMs) by 25.2%.

Key takeaway

For AI Engineers and Research Scientists developing or deploying LLM reasoning systems, Agentic Verifier offers a robust approach to improve solution reliability. By adopting its multi-agent, tool-augmented verification paradigm, you can mitigate error propagation and enhance interpretability in complex tasks. Consider integrating bidirectional checking and synthetic data generation to train more effective reward models, potentially outperforming larger, less sophisticated systems.

Key insights

Agentic Verifier uses multi-agent, tool-augmented, bidirectional reasoning to improve LLM reward modeling and verification.

Principles

• Bidirectional verification enhances reliability.
• Tool integration grounds LLM reasoning.
• Multi-turn deliberation reduces error propagation.

Method

Agentic Verifier coordinates forward and backward agents, each using a "Plan-Validate-Verdict" strategy with multi-turn reasoning and external tools like Python interpreters, distilled into a single LLM via AgentV-RL's synthetic data engine and two-stage training.

In practice

• Use forward agents for sufficiency checking.
• Employ backward agents for necessity checking.
• Integrate code interpreters for numerical validation.

Topics

• Agentic Verifier
• Reward Modeling
• Test-Time Scaling
• LLM Reasoning
• Multi-Agent Systems

Code references

• JiazhengZhang/AgentV-RL

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.