<p>The assumption is that each layer has its own set of weights. See equations (1) and (2) on page 4 <a href="https://arxiv.org/pdf/1308.0850.pdf" rel="nofollow noreferrer">here</a>. As you can see, the weights depend on the layer (the equations there deal with vanilla rnn, but the same assumption is done with LSTM).</p>
does stacked lstm or stacked RNN requires sharing the weights across layers as default?
<p>In deep-learning literature, I have encountered many examples of using stacked RNN ( stacked LSTM ) networks and while the details of the cell itself is explored, usually there is no information whether the weights are shared across different layers in a stacked architecture or not. </p>

<p>What I try to understand is that when the author does not specify this, what would be the default behavior? Should we assume that they have shared the weights across the layers? or each layer would have its own set of weights for it's cell?</p>