Efficient VLA Model Inference

VLA: Vision-Language-Action

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36 papers in the last four weeks, up 300% on the four weeks before. 0.4% of all new papers.

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ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models
May 19, 2026Arash Akbari, Arman Akbari, Masih Eskandar +11Mixed-Precision Quantization VLM Quantization
1Northeastern University · University of Georgia · 3Cisco Systems
Offline Semantic Guidance for Efficient Vision-Language-Action Policy Distillation
May 15, 2026Jin Shi, Brady Zhang, Yishun LuVLM Distillation Efficient VLA Model Inference
Department of Mechanical Engineering University College London London, United Kingdom · Department of Engineering Science University of Oxford Oxford, United Kingdom
Realtime-VLA FLASH: Speculative Inference Framework for Diffusion-based VLAs
May 13, 2026Jiahui Niu, Kefan Gu, Yucheng Zhao +5Efficient VLA Model Inference Speculative Decoding
State Key Lab of Processors, Institute of Computing Technology, CAS · University of Chinese Academy of Sciences · Dexmal +1
OOM-Free Alpamayo via CPU-GPU Memory Swapping for Vision-Language-Action Models
May 12, 2026Seungwoo Roh, Huiyeong Kim, Jong-Chan KimVLMs for Autonomous Driving Efficient VLA Model Inference
Graduate School of Automobile and Mobility, Kookmin University, Korea
LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models
May 11, 2026Boyang Shen, Kaixiang Yang, Hao Wang +4Efficient VLM Inference Efficient VLA Model Inference
1Huazhong University of Science and Technology · 2Wuhan United Imaging Surgical Co.,Ltd. (UIS)
Understanding Asynchronous Inference Methods for Vision-Language-Action Models
May 4, 2026Ayoub AgouzoulRobotic Control Efficient VLM Inference
École polytechnique · Systems Group, ETH Zürich
Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference
May 4, 2026Yudong Liu, Yuan Li, Zijia Tang +12Efficient VLM Inference Efficient VLA Model Inference
1Duke University · 2Qualcomm AI Research · University of Florida
Motubrain: An Advanced World Action Model for Robot Control
Apr 30, 2026Motubrain Team, Chendong Xiang, Fan Bao +17Robotic Control World Models for Robotics
Motubrain Team
CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies
Apr 27, 2026Fan Du, Feng Yan, Jianxiong Wu +8Robotic Control Efficient VLA Model Inference
1Southern University of Science and Technology · 2Xi’an Jiaotong University · 3United Nova Technology +1
Characterizing Vision-Language-Action Models across XPUs: Constraints and Acceleration for On-Robot Deployment
Apr 27, 2026Kaijun Zhou, Qiwei Chen, Da Peng +3AI Accelerator Inference Edge Inference
School of Computer Science, Shanghai Jiao Tong University
Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
Apr 20, 2026Jinghui Lu, Jiayi Guan, Zhijian Huang +47Trajectory Generation VLMs for Autonomous Driving
DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models
Mar 9, 2026Zihao Zheng, Hangyu Cao, Chibang Tao +9VLM Quantization Efficient VLA Model Inference
School of Computer Science, Peking University, Beijing, China. · School of Software Engineering, South China University of Technology, Guangzhou, China. · School of Artificial Intelligence, Beijing Normal University, Beijing, China. +1
DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation
Jan 29, 2026Haozhe Xie, Beichen Wen, Jiarui Zheng +4Inference-Time Optimization Efficient VLM Inference
VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
Jul 7, 2025Juyi Lin, Amir Taherin, Arash Akbari +11Robotic Control Efficient VLA Model Inference
Northeastern University, Boston, USA · EmbodyX,San Mateo,USA

May 19, 2026, cs.CVJ/K move · Enter open · S save

ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models

Arash Akbari, Arman Akbari, Masih Eskandar, Qitao Tan+10

1Northeastern University · University of Georgia · 3Cisco Systems

Mixed-Precision Quantization VLM Quantization Efficient VLA Model Inference Efficient VLA Models Vision-Language-Action Models Post-Training Quantization+2

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Vision-Language-Action (VLA) models exhibit remarkable action generation for embodied intelligence, but their heavy compute make deployment on edge platforms impractical. Aggressive, sub-4-bit weight quantization is the natural solution, yet existing post-training quantization (PTQ) methods suffer severe performance degradation in this regime. To address this, we introduce ActQuant, an action-guided mixed-precision PTQ framework that operates in two stages: (1) an inter-tensor bit allocator that assigns each weight matrix a single bit-width based on how much it contributes to predicting the agent's actions; (2) an intra-tensor scale optimizer tunes per-block quantization scales using action-aware curvature, so that dynamic range is concentrated on the weights most influential for control. To deliver the on-device benefits of our aggressive quantization, we further introduce OmniModel.cpp, an agentic conversion pipeline that ports architectures into a native C/C++ runtime with efficient low-bit kernels. We evaluate ActQuant both in simulation and on a real-world 6-DoF UR3 arm, with all models deployed through OmniModel.cpp. On the LIBERO benchmark, ActQuant is the only method that operates at or below 3 bits-per-weight, retaining 95.0% on OpenVLA-OFT and 94.8% on

π_{0.5}

. Pushed further, ActQuant reaches 2.5 bpw at 90.1% on OpenVLA-OFT, compressing the backbone from 14.3 GB to 2.7 GB (5.3

\times

). On the physical UR3 arm,

π_{0.5}

quantized with ActQuant retains the baseline's success rate while reducing the memory footprint by 2.5

\times

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