Variational inference (VI) is the problem of approximating a complicated distribution with a distribution which is simpler to sample. A major hinder for VI is mode collapse: the phenomenon where the model concentrates on a few modes of the target distribution during training, despite being expressive enough to cover all modes. This dynamical phenomenon is poorly understood theoretically. In our recent pre-print we identify different mechanisms driving mode collapse in a Gaussian mixture model setting, showing that they share some similarities with what is found in normalising flows.