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:2610.33074v1 Announce Type: cross Abstract: Vision-language-action (VLA) models enable robots to perform tasks specified through natural-language instructions, but their large weight footprint...
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: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: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:2607. 24148v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated strong potential for embodied AI, yet their high inference latency on GPUs limits real-time deployment.
Vision-Language-Action (VLA) models have demonstrated strong potential for embodied AI, yet their high inference latency on GPUs limits real-time deployment. Existing accelerators, such as Dadu-Corki, improve efficiency but treat VLA models as full-precision workloads, leaving substantial redundancy in both memory and computation underexploited.
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.
arXiv:2605. 28803v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models unify perception, reasoning, and control within a single policy, yet their multi-billion-parameter backbones and diffusion-based action heads make on-device deployment prohibitively expensive.
arXiv:2610.02666v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models map visual observations and language instructions to continuous robot actions, but a diffusion-based action expert...
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.
The paper introduces M2Tok, a Multi-head Multi-codebook Action Tokenizer that reduces reconstruction loss for continuous action signals by decomposing latent features into multiple heads and assigning independent codebooks to each. This design expands representational expressivity, leading to lower reconstruction error and higher success rates in Vision‑Language‑Action models evaluated on RoboTwin, Simpler‑Env, and zero‑shot real‑world tasks. Ablation studies confirm the effectiveness of both multi‑head and multi‑codebook mechanisms.