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LLM agents can be turned into powerful verifiers by giving them a workspace and evidence tools, outperforming baselines on complex long-horizon tasks by resolving disagreements and challenging consensus.
Static environments can be transformed on-the-fly to better suit agent learning, resulting in up to a 9.0-point performance boost with fewer execution steps.
