AI RESEARCH
Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models
arXiv CS.LG
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ArXi:2512.02636v3 Announce Type: replace Log-likelihood evaluation enables important capabilities in generative models, including model comparison, certain fine-tuning objectives, and many downstream applications. Yet paradoxically, some of today's best generative models -- diffusion and flow-based models -- still require hundreds to thousands of neural function evaluations (NFEs) to compute a single likelihood.