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 th

Diagnosing Reward Model Training Issues

Analyzing Reward Model Performance via Loss Function