How Much of Your VLA Is kNN
Overview
This research investigates the extent to which Vision-Language-Action (VLA) models rely on generalization versus retrieval. The authors show that retrieving nearby demonstrations achieves a 91.7% success rate on the LIBERO benchmark without policy training, outperforming other methods under perturbations. They introduce Local Action Consistency (LAC), an offline metric to measure action consistency in representation space, which can guide the training of retrieval keys. The study evaluates nine publicly available VLAs and provides source code at https://github.com/sereact/lerobot_knn.
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Event evidence and corrections
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Environment: simulation
“arXiv:2611.13160v1 Announce Type: new Abstract: Vision-language-action (VLA) models now exceed 95% success on the LIBERO simulation benchmark, yet it remains unclear how much of this performance requires generalization beyond the training demonstrations. In this work, we show that retrieving nearby demonstrations and reusing their action chunks achieves a success rate of 91.7 % on LIBERO without policy training. Unde”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 91.7 percent · reported trials
“arXiv:2611.13160v1 Announce Type: new Abstract: Vision-language-action (VLA) models now exceed 95% success on the LIBERO simulation benchmark, yet it remains unclear how much of this performance requires generalization beyond the training demonstrations. In this work, we show that retrieving nearby demonstrations and reusing their action chunks achieves a success rate of 91.7 % on LIBERO without policy training. Unde”
Exact source · revision 1Source owner not reported
Artifact availability · code: available
“associated with similar future actions. We show that LAC tracks the success rate of retrieval and can guide the training of a retrieval key. Our evaluation and source code are available at https://github.com/sereact/lerobot_knn.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalHow Much of Your VLA Is kNN
This research investigates the extent to which Vision-Language-Action (VLA) models rely on generalization versus retrieval. The authors show that retrieving nearby demonstrations achieves a 91.7% success rate on the LIBERO benchmark without policy training, outperforming other methods under perturbations. They introduce Local Action Consistency (LAC), an offline metric to measure action consistency in representation space, which can guide the training of retrieval keys. The study evaluates nine publicly available VLAs and provides source code at https://github.com/sereact/lerobot_knn.
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Current attention 9·Peak within the comparable range 10（2026-11-11 06:00 UTC）·Change within the comparable range over 24 hours –
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