Rethinking Losses for Diffusion Bridge Samplers

The study on diffusion bridge samplers presents a critical evaluation of loss functions used in deep learning for sampling from unnormalized distributions. It establishes that the reverse Kullback-Leibler (rKL) loss, particularly when combined with the log-derivative trick (rKL-LD), outperforms the Log Variance (LV) loss. This is crucial as it addresses the conceptual shortcomings of LV loss, which lacks a solid optimization foundation compared to rKL loss. The findings indicate that samplers trained with rKL-LD not only achieve better performance but also exhibit more stable training behavior and require significantly less hyperparameter tuning. This research is pivotal for advancing the field of deep learning, as it provides a more effective approach to training models that can sample from complex distributions, ultimately enhancing the capabilities of AI applications.