arXiv Computer Vision
What Makes an Efficient VLA? Navigating Action-Head Design, Scaling, and Latency
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CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models
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vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models
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TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM
Vision-language-action (VLA) models commonly adopt an LLM-centric $V \to L \to A$ pathway, where visual observations are projected into the representation space of a large language model before being decoded into robot actions. Although effective, this design incurs substantial computation and memory overhead at every policy invocation.
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VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models
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