Calculating Pointwise Reward Model Loss

A machine learning engineer is training a reward model where human annotators assign an absolute quality score to each generated text. The engineer considers switching the loss function from Mean Squared Error (MSE), which calculates (human_score - predicted_reward)^2, to Mean Absolute Error (MAE), which calculates |human_score - predicted_reward|. What is the most significant consequence of this change on the reward model's learned behavior?

True or False: When training a reward model using the loss function L = E[(human_score - predicted_reward)^2], the primary objective is to ensure that for any two outputs, the one with the higher human score also receives a higher predicted reward from the model.