VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via
  Iterative Instruction Tuning and Reinforcement Learning
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.
67f350a5e11bd4b05575a831
Xianwei Zhuang
67f350a5e11bd4b05575a832
Yuxin Xie
67f350a5e11bd4b05575a833
Yufan Deng
67f350a5e11bd4b05575a834
Dongchao Yang
67f350a5e11bd4b05575a835
Liming Liang
67f350a5e11bd4b05575a836
Jinghan Ru
67f350a5e11bd4b05575a837
Yuguo Yin
67f350a5e11bd4b05575a838
Yuexian Zou
VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via
  Iterative Instruction Tuning and Reinforcement Learning
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.
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.
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