During August 2025, Jiayang Song focused on optimizing expert routing performance for the Qwen-moe model within the rjg-lyh/vllm-ascend repository. He refactored the expert selection logic, integrating the arange operation more efficiently to streamline row index generation and improve the execution path across fused expert operations. Working primarily with CUDA, PyTorch, and Python, Jiayang’s changes targeted both performance and maintainability, removing redundant operations and simplifying code structure. This engineering effort addressed scalability challenges for larger workloads, enhancing routing throughput and latency, and ultimately enabled more efficient resource utilization for Qwen-moe deployments in high-concurrency deep learning environments.