AGQ: Action-Guided Ultra-Low-Bit Quantization for Vision-Language-Action Models
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SACQ is a weight‑only vector‑codebook quantization framework designed for Vision‑Language‑Action models, addressing the challenges of ultra‑low‑bit precision. It incorporates structure‑aware codebook fitting, action‑aware mixed‑bit allocation, and a two‑stage post‑quantization refinement to maintain policy performance. Experiments on GR00T N1.7 and SimplER show that SACQ achieves high success rates (up to 93.85%) while reducing memory usage by 4.83× and improving inference speed.
arXiv:2602. 13710v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models enable instruction-following embodied control, but their large compute and memory footprints hinder deployment on resource-constrained robots and edge platforms.
arXiv:2609.24433v1 Announce Type: cross Abstract: Low-bit vision-language-action inference must reduce observation-to-action latency while preserving robot behavior. We present FoldQuantVLA, a post-t...
arXiv:2605. 24011v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models exhibit remarkable action generation for embodied intelligence, but their heavy compute make deployment on edge platforms impractical.
ActQuant is a mixed‑precision post‑training quantization framework that reduces Vision‑Language‑Action model weights to sub‑4‑bit precision. It first allocates a single bit‑width to each weight matrix based on its contribution to action prediction, then optimizes per‑block scales using action‑aware curvature to focus dynamic range on control‑critical weights. Coupled with the OmniModel.cpp conversion pipeline, ActQuant enables deployment on edge devices, achieving up to 2.5 bits‑per‑weight while preserving over 90 % of baseline performance on both simulation and a real 6‑DoF UR3 robotic arm.
arXiv:2605. 28803v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models unify perception, reasoning, and control in a single policy, but their multi-billion-parameter backbones and diffusion-based action heads make on-device deployment prohibitively expensive.