DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving

Understand

Scaling Vision-Language-Action (VLA) models on large-scale data offers a promising path to achieving a more generalized driving intelligence.

• However, VLA models are limited by a ``supervision deficit'': the vast model capacity is supervised by sparse, low-dimensional actions, leaving much of their representational power underutilized.
• To remedy this, we propose \textbf{DriveVLA-W0}, a training paradigm that employs world modeling to predict future images.
• This task generates a dense, self-supervised signal that compels the model to learn the underlying dynamics of the driving environment.