Introduction Singular learning theory (SLT) is a theory of learning dynamics in Bayesian statistical models. It has been argued that SLT could provide insights into the training dynamics of deep neural networks. However, a theory of deep learning inspired by SLT is still lacking. In particular it seems important to have a better understanding of the relevance of SLT insights to stochastic gradient descent (SGD) – the paradigmatic deep learning optimization algorithm. We explore how the degeneracies[1] of toy, low dimensional loss landscapes affect the dynamics of stochastic gradient descent (SGD).[2] We also investigate the hypothesis that the set of parameters selected by SGD after a large number of gradient steps on a degenerate landscape is distributed like the Bayesian posterior at low temperature (i.e., in the large sample limit). We do so by running SGD on 1D and 2D loss landscapes with minima of varying degrees of degeneracy. While researchers experienced with SLT are aware of differences between SGD and Bayesian inference, we want to understand the influence of degeneracies on SGD with more precision and have specific examples where SGD dynamics and Bayesian inference can differ. Main takeaways * Degeneracies influence SGD dynamics in two ways: (1) Convergence to a critical point is slower, the more degenerate the critical point is; (2) On a (partially) degenerate manifold, SGD preferentially escapes along non-degenerate directions. If all directions are degenerate, then we empirically observe that SGD is "stuck" * To explain our observations, we show that, for our models, SGD noise covariance is proportional to the Hessian in the neighborhood of a critical point of the loss * Thus SGD noise covariance goes faster to zero along more degenerate directions, to leading order in the neighborhood of a critical point * Qualitatively we observe that the concentration of the end-of-training distribution of parameters sampled from a set of SGD trajector