title: VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via
    Iterative Instruction Tuning and Reinforcement Learning
summary: In this work, we present VARGPT-v1.1, an advanced unified visual
  autoregressive model that builds upon our previous framework VARGPT. The model
  preserves the dual paradigm of next-token prediction for visual understanding
  and next-scale generation for image synthesis. Specifically, VARGPT-v1.1
  integrates: (1) a novel training strategy combining iterative visual
  instruction tuning with reinforcement learning through Direct Preference
  Optimization (DPO), (2) an expanded training corpus containing 8.3M
  visual-generative instruction pairs, (3) an upgraded language model backbone
  using Qwen2, (4) enhanced image generation resolution, and (5) emergent image
  editing capabilities without architectural modifications. These advancements
  enable VARGPT-v1.1 to achieve state-of-the-art performance in multimodal
  understanding and text-to-image instruction-following tasks, demonstrating
  significant improvements in both comprehension and generation metrics. Notably,
  through visual instruction tuning, the model acquires image editing
  functionality while maintaining architectural consistency with its predecessor,
  revealing the potential for unified visual understanding, generation, and
  editing. Our findings suggest that well-designed unified visual autoregressive
  models can effectively adopt flexible training strategies from large language
  models (LLMs), exhibiting promising scalability. The codebase and model weights
  are publicly available at https://github.com/VARGPT-family/VARGPT-v1.1.
paper_authors._id: 67f350a5e11bd4b05575a831
paper_authors.name: Xianwei Zhuang
paper_authors._id: 67f350a5e11bd4b05575a832
paper_authors.name: Yuxin Xie
paper_authors._id: 67f350a5e11bd4b05575a833
paper_authors.name: Yufan Deng
paper_authors._id: 67f350a5e11bd4b05575a834
paper_authors.name: Dongchao Yang
paper_authors._id: 67f350a5e11bd4b05575a835
paper_authors.name: Liming Liang
paper_authors._id: 67f350a5e11bd4b05575a836
paper_authors.name: Jinghan Ru
paper_authors._id: 67f350a5e11bd4b05575a837
paper_authors.name: Yuguo Yin
paper_authors._id: 67f350a5e11bd4b05575a838
paper_authors.name: Yuexian Zou
paper_title: VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via
    Iterative Instruction Tuning and Reinforcement Learning
paper_summary: In this work, we present VARGPT-v1.1, an advanced unified visual
  autoregressive model that builds upon our previous framework VARGPT. The model
  preserves the dual paradigm of next-token prediction for visual understanding
  and next-scale generation for image synthesis. Specifically, VARGPT-v1.1
  integrates: (1) a novel training strategy combining iterative visual
  instruction tuning with reinforcement learning through Direct Preference
  Optimization (DPO), (2) an expanded training corpus containing 8.3M
  visual-generative instruction pairs, (3) an upgraded language model backbone
  using Qwen2, (4) enhanced image generation resolution, and (5) emergent image
  editing capabilities without architectural modifications. These advancements
  enable VARGPT-v1.1 to achieve state-of-the-art performance in multimodal
  understanding and text-to-image instruction-following tasks, demonstrating
  significant improvements in both comprehension and generation metrics. Notably,
  through visual instruction tuning, the model acquires image editing
  functionality while maintaining architectural consistency with its predecessor,
  revealing the potential for unified visual understanding, generation, and
  editing. Our findings suggest that well-designed unified visual autoregressive
  models can effectively adopt flexible training strategies from large language
  models (LLMs), exhibiting promising scalability. The codebase and model weights
  are publicly available at https://github.com/VARGPT-family/VARGPT-v1.1.
paper_ai_summary: VARGPT-v1.1, an advanced visual autoregressive model, integrates iterative visual instruction tuning with reinforcement learning, enhances resolution, and achieves state-of-the-art performance in multimodal understanding and text-to-image tasks.
paper_ai_keywords: visual autoregressive model next-token prediction next-scale generation iterative visual instruction tuning reinforcement learning Direct Preference Optimization expanded training corpus Qwen2 enhanced image generation resolution emergent image editing capabilities multimodal understanding text-to-image instruction-following tasks large language models