This paper improves upon state-of-the-art information-theoretic generalization bounds for iterative algorithms using PAC-Bayes theory. When particularized to SGLD, this machinery gives a generalization bound that scales with the trace of covariance along the trajectory of the algorithm. This is a topic of current interest, and the techniques in this paper will certainly be useful for further research.