# How does backpropagation work with averaging layers?

I'm studying Word2Vec algorithm, and so far i understood that, in the case of input context bigger than 1 (so multiple words) we have our hidden layer that performs averaging between the inputs (as explained here: Word2Vec CBOW)

I didn't understand how does backpropagation work in this case.

When i start backpropagating from the softmax output layer and i reach for the hidden layer in my process, am i coping the weight over all the "same position" inputs?

To make my question clearer, if i have 2 context words coded as one hot vectors and i call the first weight of the first word as W11 and the first weight of the second word as W21, when i'm backpropagating, those two weights will be updated with se same value, right?