You don’t need a frontier model. You need a verifier.

Agentic search systems often generate correct answers within multiple candidate responses but fail to select them, making independent verification more effective than majority voting when incorrect answers are widely repeated. On Google DeepMind’s FACTS-Search benchmark, adding a Claude Opus verifier to a fixed model pool raised performance from 83.3% to 93.4%, exceeding the cited published state of the art, while an all-open-source generator pool improved from 60.1% to 80.4%. Because frontier-model verification is costly, the researchers trained an 8B open-source verifier using supervised fine-tuning followed by reinforcement learning on roughly 6,000 balanced candidate-verdict examples, teaching it to independently search, assess each candidate, and reject unsupported answers. The trained verifier achieved 92.9% in the high-end configuration at substantially lower cost than Opus, and enabled a fully open-source setup to reach 77.0% for $0.017 per question. Tests on the separate BrowseComp-Plus benchmark indicated that verifier precision transferred to a different retrieval setting, though recall initially declined until a small domain-specific fine-tuning step improved results. The work notes limitations including small 100-question evaluation samples, automated-label noise, reliance on closed-source models for distillation, and added latency from verification.

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