A recent study introduces proximal regret, a novel concept in game theory that enhances our understanding of equilibria in online learning and games. This new approach, which sits between external and swap regret, promises to improve how players can strategize in convex games. By employing no-proximal-regret algorithms, players can achieve proximal correlated equilibria, potentially leading to more efficient outcomes in competitive scenarios. This advancement is significant as it could reshape strategies in artificial intelligence and computational theory, making it easier for systems to learn and adapt.