Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding, and data labeling. This separation makes it hard to compare related outputs, investigate model behavior, and reuse the same representations across the development workflow.
NVIDIA Alpamayo 2 Super is an open 34-billion-parameter reasoning vision-language-action (VLA) model designed to accelerate autonomous vehicle (AV) development. It combines the 32-billion-parameter NVIDIA Cosmos 3 Super Reasoner with a 2-billion-parameter diffusion-based Action Expert and is post-trained with reinforcement learning. The reasoner interprets multi-camera video, language context, and prior motion history, while the Action Expert converts the model’s resulting internal representation into a future ego-vehicle trajectory.
Alpamayo 2 Super’s perception expands to 360-degree coverage across up to seven cameras and can return multiple complementary outputs: future trajectories, Chain-of-Causation (CoC) reasoning traces, high-level meta-actions, grounded answers to questions about the scene, and reasoning auto-labels.
This multi-task design gives AV developers a common foundation across multiple stages of the development workflow. The same foundation model can be used as an offline policy teacher, an evaluation critic, a data engine, or a starting point for new task customization, instead of maintaining a separate model for each stage of the workflow.
