AgentV-RL introduces bidirectional forward-backward agents and RL-driven tool use to improve LLM verifiers, with a 4B model beating prior outcome reward models by 25.2%.
If the final value is correct, then the solution must have obtained correct values ofa and b
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AgentV-RL: Scaling Reward Modeling with Agentic Verifier
AgentV-RL introduces bidirectional forward-backward agents and RL-driven tool use to improve LLM verifiers, with a 4B model beating prior outcome reward models by 25.2%.