Three-stage Evolution and Fast Equilibrium for SGD with Non-degerate Critical Points

We justify the fast equilibrium conjecture on stochastic gradient descent from (Li et al., 2020) under the assumptions that critical points are nondegenerate and the stochastic noise is a standard Gaussian. In this case, we prove an scaling invaraint SGD with constant effective learning rate consists of three stages: descent, diffusion and tunneling, and explicitly identify temporary equilibrium states that can be observed within practical training time. This interprets the gap between the mixing time in the fast equilibrium conjecture and the previously known upper bound. While our assumptions do not represent typical implementations of SGD of neural networks in practice, this is the first description of the three-stage mechanism in any case. The main finding in this mechanism is that a temporary equilibrium of local nature is quickly achieved after polynomial time (in term of the reciprocal of the intrinsic learning rate) and then stabilizes within observable time scales; and that the temporary equilibrium is in general different from the global Gibbs equilibrium, which will only appear after an exponentially long period beyond typical training limits. Our experiments support that this mechanism may extend to the general case.