A reward model is being trained to classify text segments as either 'safe' or 'unsafe'. Consider two training approaches:

Approach A uses a loss function that only penalizes the model if its prediction for a segment is either incorrect or correct but not by a sufficient margin of confidence. Once a prediction is 'confidently correct,' no further penalty is applied.

Approach B uses a loss function that always applies a penalty for incorrect predictions and also continues to reward the model for becoming even more confident in its correct predictions, no matter how confident it already is.

Explain a potential advantage of using the max-margin approach (Approach A) over the continuous-reward approach (Approach B) for this training task.