The article discusses a new method called Pareto Mutual Distillation (Pareto-MD) that aims to improve multilingual machine translation. Instead of sacrificing performance in some languages to boost others, this approach trains two models to excel in different languages and learn from each other's strengths. By enhancing communication between these models, the researchers were able to significantly improve translation quality across multiple languages, outperforming existing methods by up to 2.46 BLEU points.