This is standard backpropagation. The gradient term you see is in fact a vector of partial derivatives where each element is the partial derivative of the log-likelihood with respect to each element of the parameter vector \theta. Therefore, it has the same dimensionality as \theta. Each element of the parameter vector is then updated with the respective term in the vector of partial derivatives, which are generally not the same.