A reward model is being trained using an objective function that aims to maximize the value of $L = -(s - r)^2$ for each data point, where 's' is a fixed target score and 'r' is the model's predicted reward. For a particular data point, the target score 's' is 5.0. The model currently predicts a reward 'r' of 4.5. How would the value of L for this data point change if the model's prediction were updated to 5.5?

Analysis of Pointwise Rating Loss Behavior

A machine learning engineer is training a reward model. The goal is for the model's output, r, to be as close as possible to a set of human-provided target scores, s. The engineer chooses the following objective function to maximize for each data point: L = - (s - r)^2. Why is maximizing this objective function an effective strategy for achieving the engineer's goal?