Qiang Zhang delivered multi-metric support in the RecMetric module for the pytorch/torchrec repository, focusing on enhancing evaluation capabilities for recommendation systems. He refactored the handling of predictions, labels, and weights within RecMetric, enabling robust and flexible computation across metrics such as CTR and NE. This work streamlined the metric computation pipeline, allowing per-metric aggregation and improved reusability of evaluation logic. Using Python and leveraging data analysis and machine learning expertise, Qiang’s changes improved evaluation fidelity and supported faster experimentation. The depth of the refactor demonstrated strong integration with the TorchRec codebase and addressed key needs in metric evaluation.