ActQuant achieves sub-4-bit (down to 2.5 bpw) quantization of VLA models via action-contribution bit allocation and curvature-based scale tuning, retaining over 90% performance on LIBERO and physical robot tasks.
H-splid: Hsic-based saliency preserving latent information decomposition.arXiv preprint arXiv:2510.20627, 2025
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ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models
ActQuant achieves sub-4-bit (down to 2.5 bpw) quantization of VLA models via action-contribution bit allocation and curvature-based scale tuning, retaining over 90% performance on LIBERO and physical robot tasks.