arXiv:2606.27755v1 Announce Type: cross Abstract: 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
The rapid advancement of large language models and their integration into robotics necessitates a deeper understanding of their efficiency and redundancy for practical, resource-constrained applications.
This research provides critical insights into optimizing AI models for robotic control, potentially leading to more efficient, cost-effective, and deployable autonomous systems.
Understanding VLA model redundancy changes how researchers and developers might approach model architecture and deployment for robotics, moving towards more streamlined and purpose-built designs.
- · Robotics companies
- · AI hardware manufacturers
- · Developers of VLA models
- · Industries adopting autonomous systems
- · Inefficient VLM deployment strategies
- · Projects with oversized AI models
More efficient VLA models could accelerate the development and deployment of advanced robotic systems.
Reduced computational requirements might lower the energy footprint and cost of robotics, enabling broader adoption.
The democratization of advanced robotics due to lower barriers could lead to new industrial capabilities and market structures.
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Read at arXiv cs.AI