Static-Dynamic Disentanglement for Efficient Multi-Frame Vision-Language-Action Models

Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for generalist robotic control. Built upon vision–language model (VLM) architectures, VLAs predict actions conditioned on visual observations and language instructions, achieving strong performance and generalization across tasks. However, VLAs face two major challenges: a limited context window for input frames and inefficient inference due to the quadratic attention complexity and large parameter counts. To this end, we propose Dysta, a framework that disentangles visual inputs into multi-level static and dynamic tokens, which enables (1) retaining a single copy of static tokens across frames to significantly reduce context length, and (2) reusing the key–value (KV) cache of static tokens through a lightweight recache gate that updates only when necessary. This design enables efficient multi-frame integration and efficient inference. In addition, we introduce a new benchmark that more effectively evaluates the multi-frame integration ability of VLAs. Experiments show that DySta improves multi-frame integration by 24.5\% across metrics on our benchmark and 23.3\% in absolute success rate on real-world memory-dependent tasks, while accelerating inference by 2.0× (with +2.3\% success rate) on simulation benchmarks and 2.2× (with +10.6\% success rate) on real-world general tasks.