From Denoising to Refining: A Corrective Framework for Vision-Language
  Diffusion Model
Authors: name: Yatai Ji, name: Teng Wang, name: Yuying Ge, name: Zhiheng Liu, name: Sidi Yang, name: Ying Shan, name: Ping Luo
Organization: Hong Kong University

Discrete diffusion models have emerged as a promising direction for
vision-language tasks, offering bidirectional context modeling and theoretical
parallelization. However, their practical application is severely hindered by a
train-inference discrepancy, which leads to catastrophic error cascades:
initial token errors during parallel decoding pollute the generation context,
triggering a chain reaction of compounding errors and leading to syntactic
errors and semantic hallucinations. To address this fundamental challenge, we
reframe the generation process from passive denoising to active refining. We
introduce ReDiff, a refining-enhanced diffusion framework that teaches the
model to identify and correct its own errors. Our approach features a two-stage
training process: first, we instill a foundational revision capability by
training the model to revise synthetic errors; second, we implement a novel
online self-correction loop where the model is explicitly trained to revise its
own flawed drafts by learning from an expert's corrections. This mistake-driven
learning endows the model with the crucial ability to revisit and refine its
already generated output, effectively breaking the error cascade. Extensive
experiments demonstrate that ReDiff significantly improves the coherence and
factual accuracy of generated content, enabling stable and efficient parallel
generation far superior to traditional denoising methods. Our codes and models
are available at https://rediff-hku.github.io/.