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TRIAGE: Efficient Vision-Language-Action Inference Needs Both Reuse and Pruning — arXiv:2610.34519

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.34519
License (as archived): CC0-1.0
Archived: 2026-10-27T08:01:28.426Z
SHA-256 of the archived text: 460efb06fb844717820baeed17787dc735f318b30d7e0275de72c525aee2b72b
Stable URL of this page: https://metraipolis.com/documento/dc_ea3400ade6bb

How to cite

TRIAGE: Efficient Vision-Language-Action Inference Needs Both Reuse and Pruning — arXiv:2610.34519 — arXiv (en), https://arxiv.org/abs/2610.34519 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ea3400ade6bb (Metraipolis archive dc_ea3400ade6bb, sha256 460efb06fb844717820baeed17787dc735f318b30d7e0275de72c525aee2b72b)

BibTeX

@misc{dcea3400ade6bb,
  title = {TRIAGE: Efficient Vision-Language-Action Inference Needs Both Reuse and Pruning — arXiv:2610.34519},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.34519},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ea3400ade6bb; sha256 460efb06fb844717820baeed17787dc735f318b30d7e0275de72c525aee2b72b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.34519. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.34519 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-27T07:55:30.910Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).