DriveFine: Refining-Augmented Masked Diffusion VLA for Accurate and Robust Driving

End-to-end autonomous driving increasingly relies on gener-ative planners, broadly classified into continuous diffusion policies anddiscrete token-based vision-language-action models (VLAs). While diffu-sion policies exploit parallel iterative noise prediction to efficiently gener-ate accurate trajectories, they suffer from cross-modal misalignment, lim-ited training efficiency, and poor generalization. Conversely, token-basedVLAs generate discrete trajectory tokens autoregressively, offering bettergeneralization but being constrained by inefficient causal reasoning, cu-mulative error, and irreversible decoding. To overcome these limitations,we propose DriveFine, a vision-language-action model that exploresmasked diffusion LLMs for trajectory planning, which significantly im-proves the efficiency, flexibility, and adaptability of trajectory generation.To address the inherent irreversibility of token decoding and improve thetrajectory quality, we introduce a plug-and-play block Mixture-of-Expert(block MoE) module, which seamlessly injects the refinement capa-bility into the dLLM at minimal cost. Through explicit expert routingand gradient isolation, DriveFine decouples generation and refinement,preventing cross-task interference. We further devise a tailored hybridreinforcement learning strategy to facilitate the effective exploration ofthe refinement expert and further explore the performance ceiling. Ex-tensive experiments on the NAVSIM v1, v2, and Navhard benchmarksdemonstrate that DriveFine exhibits strong performance, robustness andefficiency. The code will be released.