Representation Distance Bias in Reward Models: Implications and Solutions

This ArXiv paper examines the issue of representation distance bias within BT-Loss, a loss function used in reward models. The research likely contributes to a better understanding of how reward models learn and the potential pitfalls associated with their training.

• •Identifies a bias in reward models related to the distance between representations.
• •Investigates the implications of this bias on model performance.
• •Suggests potential solutions or mitigation strategies for the identified bias.