CARE 認證令 VLA 模型推理提速 9.0–10.8×
Accelerator certification speeds VLA models 10
CARE 為 VLA 模型推理加速提供認證，達到 9.0–10.8× 提速，並以 95% 信心保證至少 85.8% 原本由參考方案成功完成的任務回合仍能成功。認證流程使用 5,263 次 rollout，少於窮盡測試所需的 25,000 次。方法依賴靜態校準集，目前只在四個 LIBERO 套件及兩個智能體（Qwen3.5-9B、Llama-3.1-8B）上展示。
Bounding the risk of acceleration‑induced failure lets practitioners reap order‑of‑magnitude speedups on vision‑language‑action agents. The twist is that a formal guarantee can coexist with those gains, overturning the common belief that safety and performance are mutually exclusive.
Prior VLA accelerators have chased raw latency reductions—action chunking, visual‑token pruning, and similar tricks—while reporting only average task success. Those metrics hide failures that only manifest over closed‑loop trajectories, because a single misstep can cascade into an episode‑level collapse that the average numbers never reveal.
CARE certifies 9.0–10.8× faster inference while guaranteeing that at least 85.8 % of reference‑solved episodes remain successful with 95 % confidence, even under a user‑specified failure budget [1].
The certification process itself requires roughly five times fewer rollouts than exhaustive testing. “care also has the lowest evaluation cost, using 5,263 rather than 25,000 rollouts ( fewer) and reducing H1 evaluation from 1,000 to 113 rollouts.” [1]
The method’s guarantees rely on a static calibration set and have only been demonstrated on four LIBERO suites and two agents (Qwen3.5‑9B, Llama‑3.1‑8B), leaving open how it scales to larger, continually evolving deployments or to online adaptation scenarios [1].
Future VLA benchmark runs should replace naïve latency baselines with CARE‑certified selectors as the default evaluation protocol, and deployment pipelines ought to employ failure‑triggered reference rollouts instead of exhaustive pre‑deployment testing.
References
來源：DEV Community · dev.to