Counterfactual VLA: Self-Reflective Vision-Language-Action Model with Adaptive Reasoning

Understand

Recent reasoning-augmented Vision-Language-Action (VLA) models have improved the interpretability of end-to-end autonomous driving by generating intermediate reasoning traces.

• Yet these models primarily describe what they perceive and intend to do, rarely questioning whether their planned actions are safe or appropriate.
• This work introduces Counterfactual VLA (CF-VLA), a self-reflective VLA framework that enables the model to reason about and revise its planned actions before execution.
• CF-VLA first generates time-segmented meta-actions that summarize driving intent, and then performs counterfactual reasoning conditioned on both the meta-actions and the visual context.