Researchers have developed a new, faster method for finding the maximum likelihood estimator in exponential families when it doesn't exist in the traditional sense. By using conventional maximum likelihood computations, they can approximate the estimator in the completion of the exponential family. This method is quicker and more scalable than existing algorithms, providing faster statistical inference in cases where the maximum likelihood estimator exists in the completion of the exponential family.