What Should Robot Policies Learn to Predict? Rethinking Supervision Targets in Vision-Language-Action Learning

Vision-language-action (VLA) policies are typically trained with action prediction as the primary supervision signal, since robots ultimately execute actions. Auxiliary objectives such as latent-action (LA) and future-vision (FV) prediction are often added to improve policy learning, yet they compete with action prediction for the same training budget.

From the abstract. Our summary is in progress.