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arXiv Computer Vision By Mohammed Hassan (Celine), Zhenyang Chen (Celine), Zheng Wang (Celine), Zhixin Zhu (Celine), Tianlong Chen (Celine), Yingyan (Celine), Lin, Chaojian Li

T3R: Training-Free Two-Stage Token Refinement Towards Efficient and Robust VLA Models

Read the original on arXiv Computer Vision →

T3R is a training‑free, two‑stage token refinement framework designed to improve the efficiency and robustness of Vision‑Language‑Action (VLA) models. The first stage uses segmentation‑guided pruning to remove irrelevant visual patches, while the second stage applies saliency‑guided attention biasing to focus on action‑critical regions and language cues. Experiments on LIBERO‑Spatial, LIBERO, SIMPLER, and RoboTwin show that T3R can eliminate 85% of visual tokens with minimal loss in success rate and even improve performance under distribution shifts, achieving up to a 3.3× speedup in large‑scale settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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