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arXiv · 2609.39692

GFD-OPD: Guidance-Folded On-Policy Distillation of Diffusion Models Across Scales

Zhenxing Zhang·Jiayan Teng·Wenxu Wu·Zhuoyi Yang·Jiazheng Xu·Wendi Zheng·Jie Tang·Dan Guo·Meng Wang
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Abstract

On-policy distillation (OPD) has demonstrated two important capabilities in language models: compressing large teachers into smaller students and merging expert models into a single model. Existing diffusion OPD, however, mostly focus on the latter, with teachers and students sharing the same backbone and scale. We investigate large-to-small diffusion opd from large teachers to a small student and find that the standard recipe fails. To find the underlying cause, we propose Fixed-State KL, an effective and fair way to measure the distribution gap between student and teacher during OPD training for diffusion models. We are the first to clarify why large-to-small OPD is challenging for diffusion models: a smaller student struggles to perfectly match the distribution of a larger teacher, while classifier-free guidance can accumulate and amplify the distributional discrepancies between the student's conditional and unconditional branches and those of the teacher. To solve this problem, we propose GFD-OPD, a simple yet effective method that reduces the student-teacher gap while avoiding the error amplification of the CFG composition. Across numerous experiments, GFD outperforms previous baselines in both training efficiency and final performance, achieving state-of-the-art results on all benchmarks.

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Zhenxing Zhang, Jiayan Teng, Wenxu Wu, Zhuoyi Yang, Jiazheng Xu, Wendi Zheng, Jie Tang, Dan Guo, Meng Wang. 2026-09-30. GFD-OPD: Guidance-Folded On-Policy Distillation of Diffusion Models Across Scales. https://arxiv.org/abs/2609.39692

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