Preprint Open access
OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher
As scaling pre-training data alone yields diminishing returns, post-training is becoming increasingly important across physical AI domains such as autonomous driving. End-to-end driving policies are pre-trained in open loop with behavior cloning on human demonstrations. However, compounding errors during closed-loop de …