AutoVLA

AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning

Zewei Zhou*, Tianhui Cai*, Seth Z. Zhao, Yun Zhang, Zhiyu Huang^†, Bolei Zhou, Jiaqi Ma

University of California, Los Angeles - ^* Equal contribution, ^† Project leader

• AutoVLA integrates chain-of-thought (CoT) reasoning and physical action tokenization to directly generate planning trajectories through a unified autoregressive process, dynamically switching dual-thinking modes.
• Supervised fine-tuning (SFT) is employed to equip the model with dual thinking modes: fast thinking (trajectory-only) and slow thinking (enhanced with chain-of-thought reasoning).
• Reinforcement fine-tuning (RFT) based on Group Relative Policy Optimization (GRPO) is adopted to further enhance planning performance and efficiency, reducing unnecessary reasoning in straightforward scenarios.
• Extensive experiments across real-world and simulated datasets and benchmarks, including nuPlan, nuScenes, Waymo, and CARLA, demonstrate its competitive performance in both open-loop and closed-loop settings.

News

• 2025/06: AutoVLA paper release
• 2025/05: In the Waymo Vision-based End-to-end Driving Challenge, AutoVLA ranks highly in both RFS Overall and achieves the top RFS Spotlight score, which focuses on the most challenging scenarios.

TODO List

• AutoVLA paper.
• Reasoning data.
• Reasoning annotation code.
• AutoVLA code.
• AutoVLA checkpoints.

Citation

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@article{zhou2025autovla,
 title={AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning},
 author={Zhou, Zewei and Cai, Tianhui and Zhao, Seth Z.and Zhang, Yun and Huang, Zhiyu and Zhou, Bolei and Ma, Jiaqi},
 journal={arXiv preprint arXiv:2506.13757},
 year={2025}
}