What Makes an Efficient VLA? Navigating Action-Head Design, Scaling, and Latency
Vision-Language-Action (VLA) models combine a pretrained vision encoder, a language backbone, and an action head, but their relative contribution has not been established under controlled, latency-paired conditions. We fix the backbone families (SigLIP2 and Qwen2.5) and the training pipeline, sweep action-head design and module scale, and pair each configuration with measured on-device latency. The study yields three findings. First, action-head performance is governed primarily by initialization rather than decoder architecture, loss, or inference budget: copying the last transformer layers of the language backbone into the head is the single largest lever, at no latency cost, and the on...
| Authors | Luoyang Sun, Guoyang Xia, Fengfa Li et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 September 2026 |
| Last updated at source | 23 September 2026 |
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