Xiaochang Wu focused on improving the reliability of graph partitioning workflows in the pytorch/pytorch repository by addressing a core issue with node order consistency. Over two months, Xiaochang delivered targeted bug fixes that ensured the order of nodes in partitioned graphs matched the original graph, reducing nondeterministic behavior and enhancing reproducibility for distributed workloads. The work involved Python programming and applied principles from algorithm design and graph theory, with a strong emphasis on unit testing to validate stability. By introducing regression tests and refining partitioning logic, Xiaochang contributed depth and maintainability to a critical component of PyTorch’s infrastructure.