One use case is in the training of Diffusion Models. In the original formulation, the likelihood-maximizing objective is recast in terms of KL divergences. This

One use case is in the training of Diffusion Models. In the original formulation, the likelihood-maximizing objective is recast in terms of KL divergences. This is done because the KL divergence between Gaussians has a closed form, and the transition distributions in Diffusion Models are taken to be Gaussian, which makes the problem tenable. https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/#training https://www.assemblyai.com/blog/diffusion-models-for-machine...