One potential limitation or challenge associated with the randomness in choosing trajectories in SortedNet is the need to carefully balance exploration and exploitation. While randomness can help avoid local optima and improve convergence rates, it may also introduce instability during training if not properly managed. The selection of sub-models based on random distributions could lead to inconsistent performance across different runs or datasets, requiring additional tuning or regularization techniques to ensure robustness.