[2603.24587] DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving

Computer Science > Machine Learning arXiv:2603.24587 (cs) [Submitted on 25 Mar 2026] Title:DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving Authors:Pengxuan Yang, Yupeng Zheng, Deheng Qian, Zebin Xing, Qichao Zhang, Linbo Wang, Yichen Zhang, Shaoyu Guo, Zhongpu Xia, Qiang Chen, Junyu Han, Lingyun Xu, Yifeng Pan, Dongbin Zhao View a PDF of the paper titled DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving, by Pengxuan Yang and 13 other authors View PDF HTML (experimental) Abstract:We introduce DreamerAD, the first latent world model framework that enables efficient reinforcement learning for autonomous driving by compressing diffusion sampling from 100 steps to 1 - achieving 80x speedup while maintaining visual interpretability. Training RL policies on real-world driving data incurs prohibitive costs and safety risks. While existing pixel-level diffusion world models enable safe imagination-based training, they suffer from multi-step diffusion inference latency (2s/frame) that prevents high-frequency RL interaction. Our approach leverages denoised latent features from video generation models through three key mechanisms: (1) shortcut forcing that reduces sampling complexity via recursive multi-resolution step compression, (2) an autoregressive dense reward model operating directly on latent representations for fine-grained credit assignment, and (3) Gaussian vocabulary sampling for GRPO that co...