# LSTM Capacity: Many-to-Many vs. Many-to-One

Existing research documents LSTMs to perform poorly with timesteps > 1000 - i.e., inability to "remember" longer sequences. What's absent explicit mention is whether this applies for one or more of the following:

• Many-to-Many - return $$t$$ outputs for $$t$$ input timesteps, as with Keras' return_sequences=True
• Many-to-One - return only the last output for $$t$$ input timesteps (return_sequences=False)
• Both, stacked - with =True preceding =False

In 'both', it's unclear whether the former's input (and thus output, i.e. input to latter) should be limited to <1000 timesteps, or that it transforms input timesteps in some manner that effectively 'lightens the load' on latter's memory. For one, Keras' =False LSTM utilizes more than double the =True's number of weights; I don't know why it does so, but it does imply greater model capacity.

So, does Many-to-Many, or Many-to-Many stacked with Many-to-One LSTM - bear greater memory capacity? Experimental or theoretical insights appreciated.