Reed Li developed per-parameter weight decay functionality for the Adastar optimizer in the apple/axlearn repository, enabling more granular regularization during model training. By updating the optimizer’s API to accept decay values for individual parameters, Reed implemented logic that applies these values within the training loop while maintaining backward compatibility with existing defaults. The work included comprehensive unit tests to validate both the new per-parameter behavior and standard scenarios, ensuring robust integration. Using Python and leveraging machine learning and optimization expertise, Reed’s contribution addressed the need for fine-tuned training control, enhancing the optimizer’s flexibility for diverse model architectures and research workflows.