OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher

arXiv:2609.20756 · cs.RO, cs.CV, cs.LG · Submitted 2026-09-17 · Read on arXiv

cs.RO, cs.CV, cs.LG

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: 9 pages, 5 figures

Code: https://github.com/NVlabs/alpasim

Project page: https://01dami23.github.io/opted

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fine-Tuning — 38× in this paper · explained in 5 episodes
• Distillation — 16× in this paper · explained in 2 episodes
• Reinforcement Learning — 7× in this paper · explained in 4 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• On-Policy Distillation — 2× in this paper · explained in 3 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• VLA Models — 2× in this paper · explained in 3 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• VaViM and VaVAM: Autonomous Driving through Video Generative Modeling
• World Engine: Towards the Era of Post-Training for Autonomous Driving
• Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
• Pictura: Perspective-View Self-Play at Scale for Driving
• RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework
• Scaling Self-Play for End-to-End Driving
• TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations
• Proximal Policy Optimization Algorithms

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving