The KL term L R pushes q (z|x) close to an uninformative prior p(z): the posterior becomes more like an isotropic Gaussian noise, and less representative of their corresponding observations.
l R blocks Path A, and thus z remains uninformative during the entire training process: it starts with random initialization and then is regularized towards a random noise.