NVIDIA squeezes a driving reasoning model onto onboard silicon: closed-loop RL lifts driving performance 48.9%, with 160–190 ms pixel-to-trajectory latency
NVIDIA released Alpamayo 2 Edge, an edge-oriented reasoning vision-language-action model that pairs a 2B-parameter Nemotron reasoning backbone with a diffusion action expert to produce Chain-of-Causation reasoning and future trajectories on DRIVE AGX Thor, generating reasoning only when needed. Closed-loop reinforcement learning post-training in AlpaGym improves closed-loop driving performance by 48.9% over the best-performing checkpoint without closed-loop RL, and the optimized model reaches 160–190 ms pixel-to-trajectory inference latency depending on the amount of reasoning.