Zilinzhu focused on integrating the Qwen2.5-Math-RM-72B model into the IBM/vllm repository, expanding its capabilities for enterprise-scale inference and evaluation. The work involved developing new pooling methods and implementing a dedicated reward model, both designed to enhance large-model support within the system. Using Python and leveraging deep learning frameworks such as PyTorch, Zilinzhu contributed a feature that enables more flexible and robust model evaluation workflows. The integration addressed the need for advanced model support in production environments, demonstrating depth in model development and system integration, though the scope was limited to feature delivery without reported bug fixes during the period.