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Reward hacking is mitigated and sample quality preserved in few-step discrete diffusion models by replacing intractable KL penalties with a novel inverse-distillation regularization.
Uniform Diffusion Models aren't optimized by the objective you thought they were, and fixing this mismatch closes the gap with Masked Diffusion Models.
