@inproceedings{yu-etal-2026-frameskip,
title = "{F}rame{S}kip: Learning from Fewer but More Informative Frames in {VLA} Training",
author = "Yu, Bin and
Lian, Shijie and
Lin, Xiaopeng and
Shen, Zhaolong and
Wei, Yuliang and
Wu, Changti and
Yuan, Hang and
Liu, Haishan and
Wang, Bailing and
Huang, Cong and
Chen, Kai",
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.207/",
pages = "4345--4359",
ISBN = "979-8-89176-462-0",
abstract = "Vision-Language-Action (VLA) policies are commonly trained from dense robot demonstration trajectories, often collected through teleoperation, by sampling every recorded frame as if it provided equally useful supervision. We argue that this convention creates a temporal supervision imbalance: long low-change segments dominate the training stream, while manipulation-critical transitions such as alignment, contact, grasping, and release appear only sparsely. We introduce FrameSkip, a data-layer frame selection framework that scores trajectory frames using action variation, visual-action coherence, task-progress priors, and gripper-transition preservation, then remaps training samples toward high-importance frames under a target retention ratio. Because FrameSkip operates only in the dataloader, it leaves the VLA architecture, action head, training objective, and inference procedure unchanged. Across RoboCasa-GR1, SimplerEnv, and LIBERO, FrameSkip improves the success-retention trade-off over full-frame training and simpler frame selection variants, achieving a macro-average success rate of 76.15{\%} across the three benchmarks compared with 66.50{\%} for full-frame training while using a compressed trajectory view that retains 20{\%} of unique frames in the main setting."
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<abstract>Vision-Language-Action (VLA) policies are commonly trained from dense robot demonstration trajectories, often collected through teleoperation, by sampling every recorded frame as if it provided equally useful supervision. We argue that this convention creates a temporal supervision imbalance: long low-change segments dominate the training stream, while manipulation-critical transitions such as alignment, contact, grasping, and release appear only sparsely. We introduce FrameSkip, a data-layer frame selection framework that scores trajectory frames using action variation, visual-action coherence, task-progress priors, and gripper-transition preservation, then remaps training samples toward high-importance frames under a target retention ratio. Because FrameSkip operates only in the dataloader, it leaves the VLA architecture, action head, training objective, and inference procedure unchanged. Across RoboCasa-GR1, SimplerEnv, and LIBERO, FrameSkip improves the success-retention trade-off over full-frame training and simpler frame selection variants, achieving a macro-average success rate of 76.15% across the three benchmarks compared with 66.50% for full-frame training while using a compressed trajectory view that retains 20% of unique frames in the main setting.</abstract>
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%0 Conference Proceedings
%T FrameSkip: Learning from Fewer but More Informative Frames in VLA Training
%A Yu, Bin
%A Lian, Shijie
%A Lin, Xiaopeng
%A Shen, Zhaolong
%A Wei, Yuliang
%A Wu, Changti
%A Yuan, Hang
%A Liu, Haishan
%A Wang, Bailing
%A Huang, Cong
%A Chen, Kai
%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 yu-etal-2026-frameskip
%X Vision-Language-Action (VLA) policies are commonly trained from dense robot demonstration trajectories, often collected through teleoperation, by sampling every recorded frame as if it provided equally useful supervision. We argue that this convention creates a temporal supervision imbalance: long low-change segments dominate the training stream, while manipulation-critical transitions such as alignment, contact, grasping, and release appear only sparsely. We introduce FrameSkip, a data-layer frame selection framework that scores trajectory frames using action variation, visual-action coherence, task-progress priors, and gripper-transition preservation, then remaps training samples toward high-importance frames under a target retention ratio. Because FrameSkip operates only in the dataloader, it leaves the VLA architecture, action head, training objective, and inference procedure unchanged. Across RoboCasa-GR1, SimplerEnv, and LIBERO, FrameSkip improves the success-retention trade-off over full-frame training and simpler frame selection variants, achieving a macro-average success rate of 76.15% across the three benchmarks compared with 66.50% for full-frame training while using a compressed trajectory view that retains 20% of unique frames in the main setting.
%U https://aclanthology.org/2026.findings-emnlp.207/
%P 4345-4359
Markdown (Informal)
[FrameSkip: Learning from Fewer but More Informative Frames in VLA Training](https://aclanthology.org/2026.findings-emnlp.207/) (Yu et al., Findings 2026)
ACL
- Bin Yu, Shijie Lian, Xiaopeng Lin, Zhaolong Shen, Yuliang Wei, Changti Wu, Hang Yuan, Haishan Liu, Bailing Wang, Cong Huang, and Kai Chen. 2026. FrameSkip: Learning from Fewer but More Informative Frames in VLA Training. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 4345–4359, Budapest, Hungary. Association for Computational Linguistics.