Archived
2026-10-06CLIFT: Conformal Self-Verification for Web Agent Training and Test-Time Scaling
Yifan Zhang, Yutong Dai, Viraj Prabhu, Zhiyuan Hu, Ran Xu, Zeyuan Chen
arXiv:2610.06829v1Web agents can already click through real websites, but training them is hard because the reward signal is sparse and expensive judges are not always available. CLIFT tackles that by teaching the agent to verify its own actions in natural language. During training, it asks and answers questions about what happened in a rollout, then uses a conformal filter to keep only the verification signals that are reliable enough to trust. Those certified signals become step-level rewards. At test time, the same self-verification bank is reused to compare multiple candidate trajectories and pick the safest one, without calling an external model. The result is a single framework that improves learning and also scales inference more intelligently. That matters because web agents need to operate in messy, changing environments where you cannot supervise every step. CLIFT turns weak, costly feedback into reusable structure, and that is a practical step toward more capable open-source agents.
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