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

TL;DR

This paper introduces SD-VLA, a framework that improves long-horizon vision-language-action models by disentangling static and dynamic visual information, leading to significant efficiency gains and better long-term task performance.

Contribution

The paper proposes a novel static-dynamic disentanglement method for VLAs, reducing context size and inference complexity, and introduces a new benchmark for long-horizon temporal dependency evaluation.

Findings

Outperforms baselines with 39.8% success rate improvement on the new benchmark.

Achieves 3.9% higher success rate on the SimplerEnv benchmark.

Delivers a 2.26x inference speedup over the base VLA model.

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.