{"ID":2878695,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.18269","arxiv_id":"2508.18269","title":"FlowVLA: Visual Chain of Thought-based Motion Reasoning for Vision-Language-Action Models","abstract":"Many Vision-Language-Action (VLA) models are built upon an internal world model trained via next-frame prediction ``$v_t \\rightarrow v_{t+1}$''. However, this paradigm attempts to predict the future frame's appearance directly, without explicitly reasoning about the underlying dynamics. \\textbf{This lack of an explicit motion reasoning step} often leads to physically implausible visual forecasts and inefficient policy learning. To address this limitation, we introduce the \\textbf{Visual Chain of Thought (Visual CoT)}, a paradigm that compels the model to first reason about \\textbf{motion dynamics} before generating the future frame. We instantiate this paradigm by proposing \\textbf{FlowVLA}, an autoregressive Transformer that explicitly materializes this reasoning process as ``$v_t \\rightarrow f_t \\rightarrow v_{t+1}$'', where $f_t$ is an intermediate optical flow prediction that inherently encodes motion. By forcing the model to first follow the motion plan encoded by $f_t$, this process inherently \\textbf{aligns the pre-training objective of dynamics prediction with the downstream task of action generation.} We conduct experiments on challenging robotics manipulation benchmarks, as well as real-robot evaluations. Our FlowVLA not only generates \\textbf{more coherent and physically plausible visual predictions}, but also achieves state-of-the-art policy performance with \\textbf{substantially improved sample efficiency}, pointing toward a more principled foundation for world modeling in VLAs. Project page: https://irpn-lab.github.io/FlowVLA/","short_abstract":"Many Vision-Language-Action (VLA) models are built upon an internal world model trained via next-frame prediction ``$v_t \\rightarrow v_{t+1}$''. However, this paradigm attempts to predict the future frame's appearance directly, without explicitly reasoning about the underlying dynamics. \\textbf{This lack of an explicit...","url_abs":"https://arxiv.org/abs/2508.18269","url_pdf":"https://arxiv.org/pdf/2508.18269v3","authors":"[\"Zhide Zhong\",\"Haodong Yan\",\"Junfeng Li\",\"Xiangchen Liu\",\"Xin Gong\",\"Tianran Zhang\",\"Wenxuan Song\",\"Jiayi Chen\",\"Xinhu Zheng\",\"Hesheng Wang\",\"Haoang Li\"]","published":"2025-08-25T17:59:21Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Transformer\"]","has_code":false}