LSTM with return_sequences - "Training a model on multiple timesteps simultaneously"
Setting return_sequences to True or False depends entirely on which one is appropriate with respect to how you are going to make predictions at inference time:

If you want your model to make a prediction only when it has consumed a whole sequence as input, then you use return_sequences=False, so that your loss is only based on the output after ingesting the whole sequence.
If you want your model to be able to make predictions after any number of timesteps, you set return_sequences=True and set the loss over all the time step outputs. As we are dealing with time series, the appropriate loss could be the average squared error over all time step outputs.

About the statement &quot;With return_sequences=True the model can be trained on 24h of data at a time&quot;, I think it means that you are training the model for each of the 24h time steps, instead of the single prediction trained with return_sequences=false.