LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in
  Vision-Language Models
Authors: name: Shangqing Tu, name: Yucheng Wang, name: Daniel Zhang-Li, name: Yushi Bai, name: Jifan Yu, name: Yuhao Wu, name: Lei Hou, name: Huiqin Liu, name: Zhiyuan Liu, name: Bin Xu, name: Juanzi Li

Existing Large Vision-Language Models (LVLMs) can process inputs with context
lengths up to 128k visual and text tokens, yet they struggle to generate
coherent outputs beyond 1,000 words. We find that the primary limitation is the
absence of long output examples during supervised fine-tuning (SFT). To tackle
this issue, we introduce LongWriter-V-22k, a SFT dataset comprising 22,158
examples, each with multiple input images, an instruction, and corresponding
outputs ranging from 0 to 10,000 words. Moreover, to achieve long outputs that
maintain high-fidelity to the input images, we employ Direct Preference
Optimization (DPO) to the SFT model. Given the high cost of collecting human
feedback for lengthy outputs (e.g., 3,000 words), we propose IterDPO, which
breaks long outputs into segments and uses iterative corrections to form
preference pairs with the original outputs. Additionally, we develop
MMLongBench-Write, a benchmark featuring six tasks to evaluate the
long-generation capabilities of VLMs. Our 7B parameter model, trained with
LongWriter-V-22k and IterDPO, achieves impressive performance on this
benchmark, outperforming larger proprietary models like GPT-4o. Code and data:
https://github.com/THU-KEG/LongWriter-V