From Denoising to Refining: A Corrective Framework for Vision-Language
  Diffusion Model
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/.
68fedbcc6cdff8b857f4716e
Yatai Ji
68fedbcc6cdff8b857f4716f
Teng Wang
claimed_verified
2025-10-27T10:27:18.548Z
63a9414e32ed73936ec0a0c8
/avatars/f181e1bb480502c2680be5296a036bdd.svg
wybertwang
wybertwang
user
wybertwang
68fedbcc6cdff8b857f47170
Yuying Ge
68fedbcc6cdff8b857f47171
Zhiheng Liu
68fedbcc6cdff8b857f47172
Sidi Yang
68fedbcc6cdff8b857f47173
Ying Shan
68fedbcc6cdff8b857f47174
Ping Luo
From Denoising to Refining: A Corrective Framework for Vision-Language
  Diffusion Model
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/.
ReDiff, a refining-enhanced diffusion framework, addresses train-inference discrepancies in discrete diffusion models by enabling the model to identify and correct its own errors, improving coherence and factual accuracy in generated content.
discrete diffusion models bidirectional context modeling theoretical parallelization train-inference discrepancy catastrophic error cascades parallel decoding syntactic errors semantic hallucinations ReDiff refining-enhanced diffusion framework two-stage training process synthetic errors online self-correction loop mistake-driven learning stable and efficient parallel generation