Efficient Long-Horizon Vision-Language-Action Models via Static-Dynamic Disentanglement
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866916042011312128 |
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| author | Qiu, Weikang Lei, Huashuo Huang, Tinglin Ying, Rex |
| author_facet | Qiu, Weikang Lei, Huashuo Huang, Tinglin Ying, Rex |
| contents | 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 2.0x (with +2.3% success rate) on simulation benchmarks and 2.2x (with +10.6% success rate) on real-world general tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03983 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Long-Horizon Vision-Language-Action Models via Static-Dynamic Disentanglement Qiu, Weikang Lei, Huashuo Huang, Tinglin Ying, Rex Robotics Computer Vision and Pattern Recognition 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 2.0x (with +2.3% success rate) on simulation benchmarks and 2.2x (with +10.6% success rate) on real-world general tasks. |
| title | Efficient Long-Horizon Vision-Language-Action Models via Static-Dynamic Disentanglement |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.03983 |