Self-verification through conformal methods lets web agents learn from their own reasoning about task progress, eliminating the need for expensive judge calls at deployment while improving training efficiency.
CLIFT trains web agents to complete browser tasks by having them verify their own actions through natural-language questions, creating a reusable signal that works both during training (with sparse rewards) and at test time (without expensive external judges). The method achieves state-of-the-art results on multiple web agent benchmarks and transfers across different models.