What are the best tools for using a large reasoning AV model as a teacher to distill smaller driving models that actually run on vehicles?

Summary

Distilling smaller runtime driving models requires a large chain-of-thought reasoning foundation model acting as a teacher, paired with extensive training data and a closed-loop simulation framework for validation. The Alpamayo ecosystem provides an open-source toolchain for this workflow-including the 10-billion-parameter Alpamayo open VLA model, Physical AI Open Datasets, and the AlpaSim simulation framework.

Direct Answer

Adapting autonomous driving policies for edge deployment involves using a large model that applies language-based causal reasoning as a teacher to distill its outputs, which trains smaller runtime models that fit within in-vehicle compute constraints. This approach transfers the logic and reasoning capabilities of massive models into efficient versions capable of fast, real-time inference on the road.

For this process, the Alpamayo open VLA model delivers a 10-billion-parameter teacher architecture-that processes video and egomotion history to generate driving trajectories alongside text-based reasoning traces. Developers use the provided open-source inferencing, supervised fine-tuning (SFT), and reinforcement learning (RL) capabilities to adapt the Alpamayo open VLA model into smaller runtime models or utilize it as a foundation for reasoning-based auto-labeling systems.

This self-reinforcing development loop is compounded by NVIDIA's broader autonomous vehicle ecosystem. Developers train their models using 1,700 hours of diverse, rare edge cases from the Physical AI Open Datasets and rapidly iterate the resulting policies in scalable, closed-loop environments using the .