OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
Authors: name: Jinghui Lu, name: Jiayi Guan, name: Zhijian Huang, name: Jinlong Li, name: Guang Li, name: Lingdong Kong, name: Yingyan Li, name: Han Wang, name: Shaoqing Xu, name: Yuechen Luo, name: Fang Li, name: Chenxu Dang, name: Junli Wang, name: Tao Xu, name: Jing Wu, name: Jianhua Wu, name: Xiaoshuai Hao, name: Wen Zhang, name: Tianyi Jiang, name: Lingfeng Zhang, name: Lei Zhou, name: Yingbo Tang, name: Jie Wang, name: Yinfeng Gao, name: Xizhou Bu, name: Haochen Tian, name: Yihang Qiu, name: Feiyang Jia, name: Lin Liu, name: Yigu Ge, name: Hanbing Li, name: Yuannan Shen, name: Jianwei Cui, name: Hongwei Xie, name: Bing Wang, name: Haiyang Sun, name: Jingwei Zhao, name: Jiahui Huang, name: Pei Liu, name: Zeyu Zhu, name: Yuncheng Jiang, name: Zibin Guo, name: Chuhong Gong, name: Hanchao Leng, name: Kun Ma, name: Naiyang Wang, name: Guang Chen, name: Kuiyuan Yang, name: Hangjun Ye, name: Long Chen
Organization: Xiaomi Research

Chain-of-Thought (CoT) reasoning has become a powerful driver of trajectory prediction in VLA-based autonomous driving, yet its autoregressive nature imposes a latency cost that is prohibitive for real-time deployment. Latent CoT methods attempt to close this gap by compressing reasoning into continuous hidden states, but consistently fall short of their explicit counterparts. We suggest that this is due to purely linguistic latent representations compressing a symbolic abstraction of the world, rather than the causal dynamics that actually govern driving. Thus, we present OneVL (One-step latent reasoning and planning with Vision-Language explanations), a unified VLA and World Model framework that routes reasoning through compact latent tokens supervised by dual auxiliary decoders. Alongside a language decoder that reconstructs text CoT, we introduce a visual world model decoder that predicts future-frame tokens, forcing the latent space to internalize the causal dynamics of road geometry, agent motion, and environmental change. A three-stage training pipeline progressively aligns these latents with trajectory, language, and visual objectives, ensuring stable joint optimization. At inference, the auxiliary decoders are discarded and all latent tokens are prefilled in a single parallel pass, matching the speed of answer-only prediction. Across four benchmarks, OneVL becomes the first latent CoT method to surpass explicit CoT, delivering state-of-the-art accuracy at answer-only latency, and providing direct evidence that tighter compression, when guided in both language and world-model supervision, produces more generalizable representations than verbose token-by-token reasoning. Project Page: https://xiaomi-embodied-intelligence.github.io/OneVL