Paper recorded by Signals 4 on 2026-10-05 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-05 on arXiv · recorded by Signals 4 on 2026-10-06
Category: cs.AI · 人工智能 · first seen 2026-10-06
Open-source web agents are now strong enough to execute realistic browser tasks, but training them with reinforcement learning still depends on weak supervision: binary task success is too sparse for credit assignment, while frontier-language-model judges are too expensive to call at every step and cannot be assumed available at deployment. We introduce CLIFT, a training and test-time scaling meth