the gradient flow happily manages to ignore them and finds the good one so how is this possible so to understand how is this possible in this model we need to dig a little bit more into what is happening with the algorithm and we actually can look at the at the following plot that is showing us how does the loss function the e on the y axis change as we iterate as a function of the iteration time t and we find out that either for a high value of the noises the dynamics is stuck at some value of the loss that seems to be you know pretty flat