I can&#x27;t understand the first two layer RNN which according to the author optimized the word vectors.<p>it says:<p>During training, we can follow the gradient down into these word vectors and fine-tune the vector representations specifically for the task of generating clickbait, thus further improving the generalization accuracy of the complete model.<p>how to you follow the gradient down into these word vectors?<p>if word vectors are the input of the network, don&#x27;t we only train the weight of the network? how come the input vectors get optimized during the process?