System Optimization

System optimization focuses on improving the efficiency and performance of complex computational systems, particularly in machine learning. Current research emphasizes optimizing large language models (LLMs) and graph neural networks (GNNs) through techniques like model compression, novel parallel processing strategies (e.g., sequence parallelism), and reinforcement learning-based approaches. These advancements aim to reduce computational costs, improve training speed, and enhance the deployment of these models in resource-constrained environments, impacting fields like logistics and wireless communication. The development of automated optimization tools, informed by continuous learning and robust integration, is also a significant area of focus.