These α-divergences can be interpreted as the difference between a weighted arithmetic mean of the log likelihood ratios and the log of a weighted geometric mean of the likelihood ratios, all scaled by α, and where the weights are proportional to the likelihood ratios exponentiated by α. Although we have not attempted to demonstrate this rigorously, we postulate that such a formulation acts like a control variate, thereby helping to reduce gradient variance. Our intuition is that the first term in ( ) is effectively a self-normalized (exponentiated) importance weighted expression, which is a common trick for reducing variance in importance sampling at the expense of introducing bias, whereas the second term may act like a correction, perhaps mitigating bias similar to a control variate. Further, these α-divergences have the useful property of being invariant to the unknown normalizing constant of the target distribution.