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[Submitted on 31 Aug 2026]
Abstract:Web agents have achieved significant success in automating complex internet tasks but deploying them in real-world environments requires continuous online adaptation. Given that deploying powerful proprietary models remains commercially cost-prohibitive, practitioners must rely on lightweight local models that evolve post-deployment via online teaching from a stronger teacher. However, standard interactive feedback imposes prohibitive costs. We show that conventional trajectory-level preference optimization wastes budget on both unresolvable episodes and redundant execution turns. To resolve these inefficiencies, we propose textbf{Score-Guided Online Teaching with Budgeted Trajectory Trimming}, a budget-aware framework that systematically orchestrates textbf{when} and textbf{what} to teach. Specifically, our framework integrates a solvability-aware teacher gate to dictate textbf{when} to query the teacher model and a score-guided turn selection mechanism to decide textbf{what} informative turns to retain. Extensive experiments on MiniWoB and TimeWarp demonstrate that our method achieves comparable first-pass success while reducing teacher calls by 22.6% and student training compute by 52.1% on average. Our code is available at this https URL.
Submission history
From: Pengcheng Zheng [view email]
[v1]
Mon, 31 Aug 2026 08:40:34 UTC (286 KB)
추출 본문 · 출처: arxiv.org · https://arxiv.org/abs/2609.05513