Scaling laws and embodiment-variability interaction in VLA models

Determine the scaling laws that govern the performance and generalization of Vision-Language-Action (VLA) models as model size, data diversity, and data volume increase, and ascertain how embodiment-specific variability in hardware configurations interacts with model capacity during large-scale training and deployment.

Background

The paper presents X-VLA, a soft-prompted transformer framework for cross-embodiment vision-language-action modeling, and demonstrates consistent scaling trends with increased model capacity, data diversity, and data volume. However, the authors note that even their largest tested configuration shows no sign of saturation, suggesting further gains are possible with additional scaling.

In discussing limitations, the authors highlight that computational constraints and the limited availability of high-quality robotics data currently restrict scaling. They explicitly point out that extending X-VLA to larger capacities and broader datasets raises open questions regarding the scaling laws of VLA models and the role of embodiment-specific variability (e.g., differences in hardware configurations) in determining how model capacity translates into performance.