@inproceedings{zhang-etal-2026-teach,
title = "When and What to Teach: Budget-Aware Online Adaptation for Web Agents",
author = "Zhang, Jianwei and
Cao, Sihan and
Zheng, Pengcheng and
Wen, Ya and
Ke, Pei and
Liu, Kuien and
Gao, Shen and
Dong, Wei and
Yang, Yang and
Zhang, Chaoning",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1096/",
pages = "23190--23205",
ISBN = "979-8-89176-462-0",
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 Score-Guided Online Teaching with Budgeted Trajectory Trimming, a budget-aware framework that systematically orchestrates when and what to teach. Specifically, our framework integrates a solvability-aware teacher gate to dictate when to query the teacher model and a score-guided turn selection mechanism to decide 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 https://github.com/zjw131f1fc/budgeted-online-teaching."
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%0 Conference Proceedings
%T When and What to Teach: Budget-Aware Online Adaptation for Web Agents
%A Zhang, Jianwei
%A Cao, Sihan
%A Zheng, Pengcheng
%A Wen, Ya
%A Ke, Pei
%A Liu, Kuien
%A Gao, Shen
%A Dong, Wei
%A Yang, Yang
%A Zhang, Chaoning
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-teach
%X 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 Score-Guided Online Teaching with Budgeted Trajectory Trimming, a budget-aware framework that systematically orchestrates when and what to teach. Specifically, our framework integrates a solvability-aware teacher gate to dictate when to query the teacher model and a score-guided turn selection mechanism to decide 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 https://github.com/zjw131f1fc/budgeted-online-teaching.
%U https://aclanthology.org/2026.findings-emnlp.1096/
%P 23190-23205
Markdown (Informal)
[When and What to Teach: Budget-Aware Online Adaptation for Web Agents](https://aclanthology.org/2026.findings-emnlp.1096/) (Zhang et al., Findings 2026)
ACL
- Jianwei Zhang, Sihan Cao, Pengcheng Zheng, Ya Wen, Pei Ke, Kuien Liu, Shen Gao, Wei Dong, Yang Yang, and Chaoning Zhang. 2026. When and What to Teach: Budget-Aware Online Adaptation for Web Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 23190–23205, Budapest, Hungary. Association for Computational Linguistics.