Reducing Temporal Redundancy for Efficient Vision-Language-Action Inference

Jul 15, 2026

A new system-level acceleration strategy for Vision-Language-Action (VLA) models reduces temporal redundancy in both perception and action generation. The approach incrementally updates only dynamic scene tokens and compresses diffusion sampling to a 2-step schedule, resulting in over 2x speedup while maintaining high performance, with up to 98% success rate on manipulation benchmarks. The method was validated on multiple robotic platforms, including real robots.

Why it matters: This work offers a practical advance for real-time deployment of VLA models in robotics by significantly improving inference speed without sacrificing accuracy.

Full story at: arXiv Robotics ↗