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arXiv · 2603.07904

DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models

Zihao Zheng·Hangyu Cao·Sicheng Tian·Jiayu Chen·Maoliang Li·Xinhao Sun·Hailong Zou·Zhaobo Zhang·Xuanzhe Liu·Donggang Cao·Hong Mei·Xiang Chen
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Abstract

Vision-Language-Action (VLA) models are dominant in embodied intelligence but are constrained by inference overheads. While model quantization alleviates these bottlenecks for edge deployment, static quantization approaches remain suboptimal for VLAs due to two critical challenges: (1) Temporal-dynamic sensitivity, where fixed precision wastes resources by ignoring stage-varying error tolerances; and (2) Real-time allocation, where identifying real-time sensitivity to guide bit allocation remains unsolved. To address these challenges, we propose DyQ-VLA, a dynamic quantization framework for VLAs. Specifically, a sensitivity-aware switching strategy leverages real-time kinematic proxies to trigger the bit-width switch, while a kinematic-guided module dynamically allocates the optimal bit-width. Experiments show that DyQ-VLA requires only 30.9% of the original memory footprint while maintaining 99.5% of its original performance, achieving 1.49x simulation and up to 1.43x real-world speedups.

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Zihao Zheng, Hangyu Cao, Sicheng Tian, Jiayu Chen, Maoliang Li, Xinhao Sun, Hailong Zou, Zhaobo Zhang, Xuanzhe Liu, Donggang Cao, Hong Mei, Xiang Chen. 2026-03-09. DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models. https://arxiv.org/abs/2603.07904

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