Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can reason over more complex driving scenes and produce richer intermediate reasoning are predominantly trained in open-loop, where model outputs are directly compared to ground-truth behaviors without considering their effect on the environment.

In deployment, however, a driving policy runs in closed-loop, where every braking, steering, and navigation decision affects the environment, and small errors can compound over time.

A systematic means to address this challenge is provided by NVIDIA Alpamayo , an open portfolio of AI models, simulation frameworks, and physical AI datasets for AV development. Alpamayo includes the AlpaSim AV simulation platform and the AlpaGym closed-loop training framework.

This post explains how to train AV models in closed-loop with NVIDIA Alpamayo.…