Drop-Then-Recovery: How Redundant Are Vision-Language-Action Models?
Vision-Language-Action (VLA) models enable instruction-driven robotic manipulation, but they inherit oversized language backbones from pretrained VLMs whose capacity far exceeds what is needed for short robotic instructions. This raises a basic question: how much of a VLA model is actually necessary for closed-loop control? In this work, we study architectural redundancy in VLA models by using transformer block removal as a controlled intervention. We introduce \textbf{Drop-Then-Recovery (DTR)}, an analysis protocol that removes selected blocks from a pretrained VLA model and then fine-tunes the resulting model to measure whether the removed capacity was necessary for downstream control. ...
| Authors | Guoheng Sun, Kaixi Feng, Shwai He et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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