For a data set with 600 time steps, this stackoverflow answer 39 proposes the following training schema, where each line represents a batch with sequence_length=5 that will be trained on an RNN model:
             t=0  t=1  t=2  t=3  t=4  t=5  ...  t=598  t=599
sample       |---------------------|
sample            |---------------------|
sample                 |-----------------
...
sample                                          ----|
sample                                           ----------|

I had naively assumed that this would be excessive (as the overlap will have the model seeing each data point around sequence_length times), and thought that the following would be sufficient (say bptt sequence_length is 3 for convenience):
             t=0  t=1  t=2  t=3  t=4  t=5  t=6  t=7  ...  t=598  t=599
sample       |-----------|
sample                      |-----------|
sample                                     |-------
...
sample                                                    -----------|

The first schema now makes sense to me, as it is the only way the model will be able to see each transition between time stops at least once. If I read correctly, it also looks like get_batch does this in the word_language_model 57 example. I just wanted to verify that this is the way we should be training sequential data.
Thanks