One potential limitation of the normal map-based approach compared to other proximal-type random reshuffling methods is the reliance on the normal map as a stationarity measure. While the normal map provides a useful tool for analyzing convergence properties and establishing descent guarantees, it may introduce additional computational complexity in practice. Calculating the normal map at each iteration can be computationally expensive, especially in high-dimensional optimization problems with complex structures. Additionally, the normal map may not always accurately capture the local geometry of the objective function, leading to suboptimal convergence behavior in certain scenarios.