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Let It Go or Learn to Self-Correct: Continuous Diffusion for Constrained Discrete Tasks
Denoising Diffusion Probabilistic Models (DDPMs) generate samples by starting from noise and repeatedly denoising while keeping each update close to the current noisy state. This behavior is effective in many continuous domains, but its role is less clear for globally constrained discrete tasks, suc
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