Future research could explore optimal strategies for selecting sub-models in each iteration of SortedNet by incorporating reinforcement learning techniques or adaptive algorithms. By dynamically adjusting the probabilities of selecting different sub-models based on their performance history or other relevant metrics, researchers could potentially improve the efficiency and effectiveness of SortedNet's training process. Additionally, exploring ensemble-based approaches where multiple selection strategies are combined could further enhance the algorithm's ability to find high-performing sub-model configurations efficiently.