Efficient VLA Models

VLA: Vision-Language-Action

Efficient VLA research studies how robots and other embodied agents can connect visual observations and language instructions to actions under limited compute, memory, and latency budgets. Common approaches include compact backbones and action heads, visual-token pruning, quantization and cache reuse, and faster action generation through chunking, parallel decoding, or fewer diffusion steps. Other methods adapt computation to the task or reuse compressed memory so models can respond quickly while retaining context for long-horizon manipulation, navigation, and driving. Results on benchmarks such as LIBERO, RoboCasa, and driving and real-robot evaluations show that many methods can improve speed or reduce resource use, but efficiency must also be judged by task success, robustness, motion quality, and end-to-end execution cost.

Latest papers

106 papers17 papers in the last four weeks, up 13% on the four weeks before. 0.1% of all new papers.

DA-PTQ: Drift-Aware Post-Training Quantization for Efficient Vision-Language-Action Models
Apr 13, 2026Siyuan Xu, Tianshi Wang, Fengling Li +2Post-Training Quantization Efficient VLA Models
Tongji University Shanghai, China · University of Technology Sydney Sydney, Australia
UAV-Track VLA: Embodied Aerial Tracking via Vision-Language-Action Models
Apr 2, 2026Qiyao Zhang, Shuhua Zheng, Jianli Sun +6Visual Object Tracking Vision-Language-Action Models
Beijing Institute of Technology, Beijing, China · Flying Intelligence Team (Virtual Research Community) · Institute of Automation, Chinese Academy of Sciences, Beijing, China +4
ETA-VLA: Efficient Token Adaptation via Temporal Fusion and Intra-LLM Sparsification for Vision-Language-Action Models
Mar 26, 2026Yiru Wang, Anqing Jiang, Shuo Wang +3NavSim Efficient VLA Models
Bosch Corporate Research, Bosch (China) Investment Ltd., Shanghai, China. · School of Communication and Information Engineering, Shanghai University, Shanghai, China
From Inference Efficiency to Embodied Efficiency: Revisiting Efficiency Metrics for Vision-Language-Action Models
Mar 19, 2026Zhuofan Li, Hongkun Yang, Zhenyang Chen +4Efficient VLA Models Vision-Language-Action Models
Celine · Hong Kong University of Science and Technology · Georgia Institute of Technology
$Δ$ VLA: Prior-Guided Vision-Language-Action Models via World Knowledge Variation
Mar 9, 2026Yijie Zhu, Jie He, Rui Shao +4Vision-Language-Action Models VLMs for Robotics
Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China · Great Bay University, Dongguan 523000, China · Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China +1
SaiVLA-0: Cerebrum--Pons--Cerebellum Tripartite Architecture for Compute-Aware Vision-Language-Action
Mar 9, 2026Xiang Shi, Wenlong Huang, Menglin Zou +1Vision-Language-Action Models Efficient VLA Models
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
Compact Task-Aligned Imitation Learning for Laboratory Automation
Mar 1, 2026Kanata Suzuki, Hanon Nakamura, Kana Miyamoto +1Robot Imitation Learning Laboratory Automation
Spatial Robotics Research Center, Fujitsu Limited., Kanagawa 211-8588, Japan. · Faculty of Science and Engineering, Waseda University, Tokyo 169-8050, Japan. · National Institute of Advanced Industrial Science and Technology, Tokyo 100-8921, Japan.
Rethinking the Practicality of Vision-language-action Model: A Comprehensive Benchmark and An Improved Baseline
Feb 26, 2026Wenxuan Song, Jiayi Chen, Xiaoquan Sun +12Vision-Language-Action Models Efficient VLA Models
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. · Huazhong University of Science and Technology, Wuhan, China. · Westlake University, Hangzhou, China. +1
Efficient and Explainable End-to-End Autonomous Driving via Masked Vision-Language-Action Diffusion
Feb 24, 2026Jiaru Zhang, Manav Gagvani, Can Cui +3Autonomous Driving Planning End-to-End Autonomous Driving
Institute for Physical Artificial Intelligence (IPAI), Purdue University, West Lafayette, IN 47907, USA · College of Engineering, Purdue University, West Lafayette, IN 47907, USA · Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
Selective Perception for Robot: Task-Aware Attention in Multimodal VLA
Feb 17, 2026Young-Chae Son, Jung-Woo Lee, Yoon-Ji Choi +2Efficient VLA Models Vision-Language-Action Models
Dongguk University
ActionCodec: What Makes for Good Action Tokenizers
Feb 17, 2026Zibin Dong, Yicheng Liu, Shiduo Zhang +8Efficient VLA Models Vision-Language-Action Models
Knowin AI (Work done during an internship) · Tsinghua University · Tianjin University +2
Think Proprioceptively: State-Grounded Visual Token Selection for VLA Policies
Feb 6, 2026Fangyuan Wang, Peng Zhou, Jiaming Qi +4Efficient VLM Inference Visuomotor Policy Learning
The Hong Kong Polytechnic University · Great Bay University · Northeast Forestry University +2
A Survey on Efficient Vision-Language-Action Models
Oct 27, 2025Zhaoshu Yu, Bo Wang, Pengpeng Zeng +7Model Compression Robotic Data Collection
School of Computer Science and Technology, Tongji University, China · School of Computing and Artificial Intelligence, Southwest Jiaotong University, China · School of Computer Science and Engineering, University of Electronic Science and Technology of China, China +1
StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling
Jul 7, 2025Meng Wei, Chenyang Wan, Xiqian Yu +9Efficient VLM Inference Efficient VLA Models
1Shanghai AI Lab · 2The University of Hong Kong · 3Zhejiang University +1
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

Apr 13, 2026J/K move · Enter open · S save

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

Siyuan Xu, Tianshi Wang, Fengling Li, Lei Zhu+1

Tongji University Shanghai, China · University of Technology Sydney Sydney, Australia

Post-Training Quantization Efficient VLA Models Vision-Language-Action Models Mixed-Precision Quantization

Open paper AskPDF arXiv

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.