Core Insights - The article emphasizes the importance of efficiency in Vision-Language-Action (VLA) models, which are crucial for enabling robots to understand their environment and execute tasks effectively. It identifies efficiency as a key bottleneck that hinders the transition of VLA models from research to practical applications [3][4][7]. Background and Value - The rapid development of embodied intelligence has led to the emergence of VLA models as a core framework for robotic task execution. However, current VLA systems face significant challenges related to computational and storage demands, as well as high inference latency, which are critical for real-time applications [3][4][7]. Efficiency Bottlenecks - The review systematically analyzes the efficiency issues in VLA models across four dimensions: model architecture, perception features, action generation, and training/inference processes. It highlights that efficiency challenges are systemic and not limited to single-point optimizations [3][4][7]. Classification Framework - The article categorizes existing efficient VLA strategies into four complementary dimensions: efficient architecture design, perception feature compression, action generation acceleration, and training/inference optimization. This classification provides a comprehensive understanding of the design logic and trade-offs of current methods [4][6][7]. Future Trends and Directions - The review outlines future directions for VLA models, emphasizing the need for a balance between capability enhancement and computational cost. Key areas for efficiency optimization include data utilization, perception features, action generation, and learning strategies [4][25][26]. Efficient Perception Features - Optimizing visual input, which constitutes the largest computational overhead in VLA models, can be approached through selective processing of features and temporal feature reuse. These strategies aim to reduce redundant calculations while maintaining performance [13][15][16]. Efficient Action Generation - Action generation strategies focus on minimizing latency while ensuring task accuracy. Techniques include outputting low-dimensional continuous action vectors and introducing explicit reasoning to enhance interpretability and generalization across tasks [18][21]. Efficient Training and Inference - Training strategies aim to reduce adaptation costs for new tasks and environments through methods like parameter-efficient fine-tuning and knowledge distillation. Inference strategies focus on breaking the autoregressive bottleneck to enable parallelization and mixed decoding [22][24]. Future Outlook - The article suggests that future VLA models should prioritize collaborative design between models and data, efficient spatiotemporal perception, and robust action encoding. It also calls for a standardized evaluation framework to measure efficiency improvements [25][26][27].