Efficient VLA Models

VLA: Vision-Language-Action

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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
PokeVLA: Empowering Pocket-Sized Vision-Language-Action Model with Comprehensive World Knowledge Guidance
Apr 22, 2026Yupeng Zheng, Xiang Li, Songen Gu +12Robot Foundation Models Efficient VLA Models
CASIA · TARS Robotics · Tsinghua University +3
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
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
VLA-Precision: Asymmetric Co-Bootstrapping for Efficient Real-World Online RL of Vision-Language-Action Models
Date pendingChenyu Su, Zhaolong Shen, Yuan Qian +8Efficient VLA Models Laboratory Automation
University of Science and Technology of China · Beihang University · Zhongguancun Academy +2

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CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies

Fan Du, Feng Yan, Jianxiong Wu, Xinrun Xu+7

1Southern University of Science and Technology · 2Xi’an Jiaotong University · 3United Nova Technology+1

Robotic Control Efficient VLA Model Inference Visuomotor Policy Learning Efficient VLA Models Robot Motion Generation Vision-Language-Action Models

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Flow-based vision-language-action (VLA) policies offer strong expressivity for action generation, but suffer from a fundamental inefficiency: multi-step inference is required to recover action structure from uninformative Gaussian noise, leading to a poor efficiency-quality trade-off under real-time constraints. We address this issue by rethinking the role of the starting point in generative action modeling. Instead of shortening the sampling trajectory, we propose CF-VLA, a coarse-to-fine two-stage formulation that restructures action generation into a coarse initialization step that constructs an action-aware starting point, followed by a single-step local refinement that corrects residual errors. Concretely, the coarse stage learns a conditional posterior over endpoint velocity to transform Gaussian noise into a structured initialization, while the fine stage performs a fixed-time refinement from this initialization. To stabilize training, we introduce a stepwise strategy that first learns a controlled coarse predictor and then performs joint optimization. Experiments on CALVIN and LIBERO show that our method establishes a strong efficiency-performance frontier under low-NFE (Number of Function Evaluations) regimes: it consistently outperforms existing NFE=2 methods, matches or surpasses the NFE=10

π_{0.5}

baseline on several metrics, reduces action sampling latency by 75.4%, and achieves the best average real-robot success rate of 83.0%, outperforming MIP by 19.5 points and

π_{0.5}

by 4.0 points. These results suggest that structured, coarse-to-fine generation enables both strong performance and efficient inference. Our code is available at https://github.com/EmbodiedAI-RoboTron/CF-VLA.

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