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 (coming soon). This post explains how to train AV models in closed-loop with NVIDIA Alpamayo. Specifically, it walks through how to: - Install and configure AlpaGym - Define closed-loop rewards - Launch closed-loop training - Export the post-trained checkpoint for downstream use Closed-loop post-training with AlpaGym extends AV training workflows by turning AlpaSim rollouts into training experience. Rather than treating simulation only as a final evaluation stage, AlpaGym connects simulator feedback directly to the policy training loop. How to use AlpaGym for closed-loop reinforcement learning Reinforcement learning (RL) can be used to improve a policy that was initially trained in open-loop. Instead of optimizing only against logged expert trajectories, the model can now learn from the consequences of its own actions in simulation. This shift is critical for AV development, where small prediction or planning errors can compound over time. In closed-loop training, each braking, steering, and navigation decision affects the next state of the environment, revealing failure modes that static datasets or open-loop evaluation may miss. However, enabling closed-loop RL comes with its own challenges. Model inference, running simulation, training models, syncing