During April 2025, Zxfan Zhang focused on stabilizing distributed FusedMoE parallelism in the bytedance-iaas/vllm repository by addressing a critical bug that prevented expert parallelism from activating correctly. Zhang analyzed the interaction between tensor parallelism and data parallelism, implementing a fix in Python that enabled expert parallelism only when the product of these parallelism sizes exceeded one. This adjustment improved model execution efficiency and hardware utilization for MoE workloads. The work demonstrated depth in distributed debugging, performance tuning, and PyTorch-based deep learning, reflecting a strong understanding of parallelism strategies and their impact on large-scale machine learning systems.