Model's loss weights
This will affect how the backpropagation for each of these outputs will cause the intermediate nodes within the network to be updated. If the output nodes' error were equal then the gradient descent process of the intermediate nodes will favor the resulting error of each of these outputs equally. By more heavily weighting an output node it means that the gradient descent will favor a set of parameters that perform better on that output node.