Recent research in model optimization is increasingly focused on enhancing the efficiency and performance of large neural networks, addressing critical challenges in deployment and resource management. Techniques such as Prefill-Only Pruning are demonstrating how stage-aware strategies can significantly reduce computational costs during inference without sacrificing accuracy, while methods like FlashOptim are optimizing memory usage during training, making it feasible to work with larger models on limited hardware. Additionally, the emergence of adaptive frameworks, such as Routing the Lottery, is enabling the discovery of specialized subnetworks tailored to diverse data inputs, thereby improving model performance and reducing parameter counts. Innovations in quantization, exemplified by Quant Experts, are also refining how models handle memory and computational overhead, ensuring that large vision-language models remain effective even under constraints. Collectively, these advancements signal a shift toward more modular, efficient, and context-aware deep learning architectures, poised to meet the demands of real-world applications.