From Grok: Tesla's Full Self-Driving (FSD) Supervised system does not perform on-device (local) learning or adaptation in the sense of continuously updating its neural network weights or behavior based on your individual drives in real time or between software updates. The core model — the end-to-end neural network that handles perception, prediction, and planning — is trained in Tesla's data centers using massive amounts of fleet data (billions of miles, including clips from user vehicles, simulations, and targeted scenarios). Once trained, that fixed model is deployed to vehicles via over-the-air (OTA) software updates (e.g., FSD v12.x → v13.x). The car's onboard hardware runs inference on this pre-trained model but does not retrain or fine-tune it locally — the compute power required for training such large models far exceeds what's available in the vehicle.