Efficient Long-Horizon Vision-Language-Action Models via Static-Dynamic Disentanglement

• URL: http://arxiv.org/abs/2602.03983v1
• Date: Tue, 03 Feb 2026 20:17:47 GMT
• Title: Efficient Long-Horizon Vision-Language-Action Models via Static-Dynamic Disentanglement
• Authors: Weikang Qiu, Tinglin Huang, Aosong Feng, Rex Ying,
• Abstract summary: Vision-Language-Action (VLA) models have emerged as a promising paradigm for generalist robotic control.<n>We propose SD-VLA, a framework that disentangles visual inputs into multi-level static and dynamic tokens.<n>We introduce a new benchmark that more effectively evaluates the long-horizon temporal dependency modeling ability of VLAs.
• Score: 27.517125673741486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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: limited long-horizon context and inefficient inference due to the quadratic attention complexity and large parameter counts. Our work is motivated by the observation that much of the visual information in a trajectory remains static across timesteps (e.g., the background). Leveraging this property, we propose SD-VLA, 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 long-horizon temporal dependency modeling ability of VLAs. Experimental results show that our approach outperforms baselines on this benchmark by 39.8% absolute improvement in success rate, and achieves a 3.9% gain on the SimplerEnv benchmark. Moreover, SD-VLA delivers a 2.26x inference speedup over the base VLA model on the same benchmark, enabling faster and more practical real-world deployment.

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