@inproceedings{fan-etal-2026-skill,
title = "Skill-Guided Continuation Distillation for {GUI} Agents",
author = "Fan, Zhimin and
Yu, Hongwei and
Shen, Yeqing and
Yan, Haolong and
Peng, Guozhen and
Peng, Tianhao and
Zhang, Yudong and
Zhang, Xiaowen and
Tan, Kaijun and
Ge, Zheng and
Zhang, Xiangyu and
Jiang, Daxin",
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.1566/",
pages = "33355--33373",
ISBN = "979-8-89176-462-0",
abstract = "Improving GUI agents typically relies on behavior cloning on expert trajectories. However, as the current policy deviates from the expert policy, it inevitably encounters policy-induced off-trajectory states during closed-loop execution, i.e., states that fall outside the expert trajectories. Since expert trajectories provide no demonstrations for these unseen states, such states receive no effective supervision, leaving the policy unable to select the correct action. To close this supervision gap, we propose Skill-Guided Continuation Distillation (SGCD), an iterative self-improvement framework. SGCD first runs the plain policy without skill guidance for a few steps to reach realistic off-trajectory states. From these states, a skill-guided policy then completes the task and produces successful continuations, which are mixed with expert trajectories to supply supervision over policy-induced off-trajectory states. The skills are extracted from both successful and failed rollouts, consisting of Continuation Plans, Critical Targets, Failure Traps, and Success Criteria. On OSWorld-Verified, SGCD improves the success rate of three base models from the low-30{\%} range to over 50{\%}, demonstrating its effectiveness and generality."
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<abstract>Improving GUI agents typically relies on behavior cloning on expert trajectories. However, as the current policy deviates from the expert policy, it inevitably encounters policy-induced off-trajectory states during closed-loop execution, i.e., states that fall outside the expert trajectories. Since expert trajectories provide no demonstrations for these unseen states, such states receive no effective supervision, leaving the policy unable to select the correct action. To close this supervision gap, we propose Skill-Guided Continuation Distillation (SGCD), an iterative self-improvement framework. SGCD first runs the plain policy without skill guidance for a few steps to reach realistic off-trajectory states. From these states, a skill-guided policy then completes the task and produces successful continuations, which are mixed with expert trajectories to supply supervision over policy-induced off-trajectory states. The skills are extracted from both successful and failed rollouts, consisting of Continuation Plans, Critical Targets, Failure Traps, and Success Criteria. On OSWorld-Verified, SGCD improves the success rate of three base models from the low-30% range to over 50%, demonstrating its effectiveness and generality.</abstract>
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%0 Conference Proceedings
%T Skill-Guided Continuation Distillation for GUI Agents
%A Fan, Zhimin
%A Yu, Hongwei
%A Shen, Yeqing
%A Yan, Haolong
%A Peng, Guozhen
%A Peng, Tianhao
%A Zhang, Yudong
%A Zhang, Xiaowen
%A Tan, Kaijun
%A Ge, Zheng
%A Zhang, Xiangyu
%A Jiang, Daxin
%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 fan-etal-2026-skill
%X Improving GUI agents typically relies on behavior cloning on expert trajectories. However, as the current policy deviates from the expert policy, it inevitably encounters policy-induced off-trajectory states during closed-loop execution, i.e., states that fall outside the expert trajectories. Since expert trajectories provide no demonstrations for these unseen states, such states receive no effective supervision, leaving the policy unable to select the correct action. To close this supervision gap, we propose Skill-Guided Continuation Distillation (SGCD), an iterative self-improvement framework. SGCD first runs the plain policy without skill guidance for a few steps to reach realistic off-trajectory states. From these states, a skill-guided policy then completes the task and produces successful continuations, which are mixed with expert trajectories to supply supervision over policy-induced off-trajectory states. The skills are extracted from both successful and failed rollouts, consisting of Continuation Plans, Critical Targets, Failure Traps, and Success Criteria. On OSWorld-Verified, SGCD improves the success rate of three base models from the low-30% range to over 50%, demonstrating its effectiveness and generality.
%U https://aclanthology.org/2026.findings-emnlp.1566/
%P 33355-33373
Markdown (Informal)
[Skill-Guided Continuation Distillation for GUI Agents](https://aclanthology.org/2026.findings-emnlp.1566/) (Fan et al., Findings 2026)
ACL
- Zhimin Fan, Hongwei Yu, Yeqing Shen, Haolong Yan, Guozhen Peng, Tianhao Peng, Yudong Zhang, Xiaowen Zhang, Kaijun Tan, Zheng Ge, Xiangyu Zhang, and Daxin Jiang. 2026. Skill-Guided Continuation Distillation for GUI Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 33355–33373, Budapest, Hungary. Association for Computational Linguistics.