TRIAGE：高效视觉-语言-动作推理需要复用与剪枝并用
TRIAGE: Efficient Vision-Language-Action Inference Needs Both Reuse and Pruning
AI 导读
论文提出 TRIAGE，一种针对视觉-语言-动作（VLA）模型的无训练推理加速方法，根据 token 的 drift（浅层 value 变化）判断缓存是否可复用，并结合重计算、复用与剪枝三种策略。
正文 · 原文
Abstract:Vision-language-action (VLA) models are increasingly used for robot manipulation because they map camera images and a language instruction directly to actions. Robots that cannot be tethered to a workstation, such as mobile manipulators and humanoids, must run them on onboard embedded boards, where the prefill latency of the language backbone grows with the hundreds of visual tokens processed at every control step. To meet real-time limits, training-free accelerators recompute only a subset of the visual tokens and skip the rest, either by pruning them or by reusing their cached key-value (KV) states. The two options save nearly the same computation, yet existing methods apply only one of them to every skipped token. Through a token-level diagnostic, we show that which option keeps the action closer to full recomputation depends on the token's drift, the change in its shallow-layer value since its cache was computed. On both backbones tested, reusing tokens below a drift threshold was no worse than pruning them, whereas beyond it, reuse yields up to 4.4x the action error of pruning; the same diagnostic calibrates this threshold per backbone. Building on this finding, we propose TRIAGE, which deems a cache valid when its drift is below this threshold and it is not too old. TRIAGE recomputes a fixed budget of tokens, prioritizing important tokens with invalid caches, reuses every other token with a valid cache, and prunes the rest; prune-only and reuse-only methods are the two extremes of this partition. Because the budget fixes the number of recomputed tokens, it directly sets the prefill latency. On LIBERO and SimplerEnv, TRIAGE stays within 3 pp of full recomputation in average success rate while cutting prefill latency on a Jetson AGX Orin by up to 48%, and with only 7% of visual tokens recomputed, it beats the strongest baseline by 24.4 pp on LIBERO-Object.
| Comments: | 8 pages, 6 figures |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.34519 [cs.RO] |
| (or arXiv:2610.34519v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34519 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sooyoung Kwon [view email]
[v1]
Sun, 25 Oct 2026 19:22:02 UTC (1,589 KB)
来源：arXiv cs.AI · arxiv.org