What does "immediate vector-valued feedback" mean? In the book Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning, James Stone says


  With supervised learning, the response to each input vector is an output vector that receives immediate vector-valued feedback specifying the correct output, and this feedback refers uniquely to the input vector just received; in contrast, each reinforcement learning output vector (action) receives scalar-valued feedback often sometime after the action, and this feedback signal depends on actions taken before and after the current action.


I fail to understand the part formatted in bold. Once we have a set of labeled examples (feature vector and label pairs), where is the "feedback" coming from? Testing and validation results of our calibrated model (say a neural network based one)?
By "immediate vector-valued feedback", they probably mean exactly the label in the "labeled examples" you mentioned.