AI RESEARCH
Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning
arXiv CS.LG
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ArXi:2602.08689v2 Announce Type: replace Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network. Once the denoiser is fixed, the sampling algorithm itself (noise schedules, guidance scales, stochasticity profiles) still requires careful tuning, a process typically carried out through costly empirical grid search. In this work, we