D2PO: Optimizing Diffusion Samplers via Dynamic Preference
We propose D2PO (Dynamic Direct Preference Optimization), a principled framework for optimizing diffusion sampling policies with respect to timestep schedules and classifier-free guidance (CFG) weights. Our work is motivated by a fundamental limitation of existing student-teacher regression frameworks; low-NFE student samplers are trained to mimic high-NFEteachers, often sacrificing high-frequency texture fidelity while preserving coarse global structures, thereby misaligning the sampler with pe
Record details
Published: 7 July 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Children & education
Retrieved: 14 July 2026
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ethics.ai (7 July 2026), “D2PO: Optimizing Diffusion Samplers via Dynamic Preference,” evidence record 172, https://ethics.ai/record/172 (originally published by arXiv).
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