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%.
The system will execute it and return the results for you to continue in the next turn
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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%.