Empirical Formulation of Pair-wise Ranking Loss

Empirical Pair-wise Ranking Loss for RLHF Reward Model

Regularized Pairwise Loss Function for Reward Model Training

A reward model is being trained to prefer one machine-generated text response over another for a given input. The training process aims to minimize a loss function calculated as the negative logarithm of a sigmoid applied to the difference between the reward scores of the preferred ($R_{pref}$) and non-preferred ($R_{non-pref}$) responses. Given the following reward scores assigned by the model to a single pair of responses, which scenario contributes the least to the total loss, indicating the model is correctly differentiating between the responses?

Diagnosing Reward Model Training Issues

Analyzing Reward Model Performance via Loss Function