The PAFO framework addresses bias in personalized reward models for large language models by optimizing for Pareto fairness, ensuring better representation for under-served user groups. This approach enhances accuracy for both minority and majority users while reducing unfairness, potentially improving user satisfaction and model performance in AI applications.