Ok but it's already known that you shouldn't initialize your network parameters to a single constant and instead initialize the parameters with random numbers.
Ok but it's already known that you shouldn't initialize your network parameters to a single constant and instead initialize the parameters with random numbers.
The model can converge towards such a state even if randomly initialized.
Both you and the comment above are correct; initializing with iid elements ensures that correlations are not disastrous for training, but strong correlations are baked into the weights during training, so pretty much anything could potentially happen.