DA-PTQ: Drift-Aware Post-Training Quantization for Efficient Vision-Language-Action Models

• URL: http://arxiv.org/abs/2604.11572v1
• Date: Mon, 13 Apr 2026 14:51:43 GMT
• Title: DA-PTQ: Drift-Aware Post-Training Quantization for Efficient Vision-Language-Action Models
• Abstract summary: Drift-Aware Post-Training Quantization (DA-PTQ) formulates quantization as a drift-aware optimization problem over sequential decision processes.<n>DA-PTQ significantly reduces kinematic drift and achieves comparable performance to full-precision models under low-bit settings.
• Score: 61.26694413585136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action models (VLAs) have demonstrated strong potential for embodied AI, yet their deployment on resource-limited robots remains challenging due to high memory and computational demands. While Post-Training Quantization (PTQ) provides an efficient solution, directly applying PTQ to VLAs often results in severe performance degradation during sequential control. We identify temporal error accumulation as a key factor, where quantization perturbations at the vision-language-to-action interface are progressively amplified, leading to kinematic drift in executed trajectories. To address this issue, we propose Drift-Aware Post-Training Quantization (DA-PTQ), which formulates quantization as a drift-aware optimization problem over sequential decision processes. DA-PTQ consists of two components: (1) Cross-Space Representation Compensation, which mitigates structured distortions between multimodal representations and action space to improve action consistency, and (2) Motion-Driven Mixed-Precision Allocation, which assigns bit-widths by minimizing trajectory-level motion errors. Extensive experiments show that DA-PTQ significantly reduces kinematic drift and achieves comparable performance to full-precision models under low-bit settings, enabling practical deployment of VLAs on resource-limited robotic platforms.

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