NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation
We present NextFlow, a unified decoder-only autoregressive transformer trained on 6 trillion interleaved text-image discrete tokens. By leveraging a unified vision representation within a unified autoregressive architecture, NextFlow natively activates multimodal understanding and generation capabilities, unlocking abilities of image editing, interleaved content and video generation. Motivated by the distinct nature of modalities - where text is strictly sequential and images are inherently hierarchical - we retain next-token prediction for text but adopt next-scale prediction for visual generation. This departs from traditional raster-scan methods, enabling the generation of 1024x1024 images in just 5 seconds - orders of magnitude faster than comparable AR models. We address the instabilities of multi-scale generation through a robust training recipe. Furthermore, we introduce a prefix-tuning strategy for reinforcement learning. Experiments demonstrate that NextFlow achieves state-of-the-art performance among unified models and rivals specialized diffusion baselines in visual quality.
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Huichao Zhang
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Liao Qu
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2026-01-06T09:57:57.686Z
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Liao Qu
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Yiheng Liu
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Hang Chen
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Yangyang Song
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Yongsheng Dong
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Shikun Sun
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Xian Li
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Xu Wang
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Yi Jiang
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2026-01-06T09:57:55.158Z
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Yi Jiang
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JiangYi
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Hu Ye
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Bo Chen
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Yiming Gao
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Peng Liu
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Akide Liu
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Zhipeng Yang
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Qili Deng
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Linjie Xing
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Jiyang Liu
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Zhao Wang
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Yang Zhou
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Mingcong Liu
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Yi Zhang
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Qian He
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Xiwei Hu
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Zhongqi Qi
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Jie Shao
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Zhiye Fu
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Shuai Wang
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Fangmin Chen
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Xuezhi Chai
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Zhihua Wu
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Yitong Wang
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Zehuan Yuan
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Daniel K. Du
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Xinglong Wu
NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation
We present NextFlow, a unified decoder-only autoregressive transformer trained on 6 trillion interleaved text-image discrete tokens. By leveraging a unified vision representation within a unified autoregressive architecture, NextFlow natively activates multimodal understanding and generation capabilities, unlocking abilities of image editing, interleaved content and video generation. Motivated by the distinct nature of modalities - where text is strictly sequential and images are inherently hierarchical - we retain next-token prediction for text but adopt next-scale prediction for visual generation. This departs from traditional raster-scan methods, enabling the generation of 1024x1024 images in just 5 seconds - orders of magnitude faster than comparable AR models. We address the instabilities of multi-scale generation through a robust training recipe. Furthermore, we introduce a prefix-tuning strategy for reinforcement learning. Experiments demonstrate that NextFlow achieves state-of-the-art performance among unified models and rivals specialized diffusion baselines in visual quality.
NextFlow is a unified decoder-only autoregressive transformer that processes interleaved text-image tokens, enabling fast multimodal generation through novel next-token and next-scale prediction strategies.
decoder-only autoregressive transformer interleaved text-image discrete tokens unified vision representation multimodal understanding multimodal generation next-token prediction next-scale prediction raster-scan methods visual generation prefix-tuning strategy reinforcement learning diffusion baselines