More verbose description of WeightAveraging

Author: Seppo Enarvi

docs/source-pytorch/advanced/training_tricks.rst

@@ -61,8 +61,8 @@ end up in a local minimum during optimization.
 Lightning provides two callbacks to facilitate weight averaging. :class:`~lightning.pytorch.callbacks.WeightAveraging`
 is a generic callback that wraps the
 `AveragedModel <https://pytorch.org/docs/stable/generated/torch.optim.swa_utils.AveragedModel.html>`__ class from
-PyTorch. It allows SWA, EMA, or a custom averaging strategy to be used and it can be customized to run at specific steps
-or epochs.
+PyTorch. It allows SWA, EMA, or a custom averaging strategy to be used. By default, it updates the weights after every
+step, but it can be customized to update at specific steps or epochs by overriding the `should_update()` method.
 
 The older :class:`~lightning.pytorch.callbacks.StochasticWeightAveraging` callback is specific to SWA. It starts the SWA
 procedure after a certain number of epochs and always runs on every epoch. Additionally, it switches to a constant