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Learning to Re-Draft: A Variational Stackelberg Game for Discrete Diffusion
Discrete diffusion models offer the ability to re-draft, revisiting and correcting earlier tokens throughout generation. This capability depends on the forward corruption process that defines what the denoiser learns to correct. Masked diffusion models fix tokens once they are unmasked, while unifor
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