During January 2025, Xingyao Wang developed two core features focused on scalable execution and efficient model fine-tuning. In the Furion-cn/sglang repository, he introduced generator-based incremental execution for batch processing, leveraging Python concurrency and ThreadPoolExecutor to enable concurrent, chunked results and reduce latency. For volcengine/verl, he implemented Low-Rank Adaptation (LoRA) support for supervised fine-tuning, integrating the peft library and adaptive FSDP wrapping to lower resource requirements and broaden deployment options. His work included comprehensive end-to-end testing with Qwen2.5 on GSM8K, demonstrating depth in backend development, distributed training, and configuration management using Python and shell scripting.