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Research questionHow can few-step visual generators preserve preference-aligned quality without being capped by multi-step teacher distillation?Compressing multi-step denoising into a few steps can reduce visual fidelity. Conventional distillation also makes the student imitate the teacher, allowing the teacher’s quality to become a ceiling even when reward preferences favor different outputs.
Diffusion Models
Image Generation
Inference Optimization
Machine Learning
Latest papersRecent research connected to this question, newest first.Reward-Aware Trajectory Shaping for Few-step Visual GenerationThe evidence concerns few-step visual generation using teacher and student latent trajectories aligned at selected denoising stages, with guidance adjusted according to their relative reward performance. Experiments report an improved efficiency–quality trade-off and a smaller gap to multi-step generators; broader modalities and deployment settings are not specified.research paper · Sep 4, 2026
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