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Uncertainty-guided feedback can dramatically improve the reliability of reward models in visual diffusion, leading to superior optimization and quality outcomes.
Stop blasting your diffusion models with a single, static reward signal: fine-grained credit assignment across denoising steps and objectives unlocks better image and video generation.
