Worked on the huggingface/trl repository to address a critical issue in the GKDTrainer’s loss calculation logic, specifically targeting the batchmean reduction scenario. Using Python and leveraging deep learning and model training expertise, implemented a fix that corrected the divisor used when labels are absent, thereby improving the accuracy and robustness of loss computations. This adjustment reduced the risk of misleading metrics during training experiments and enhanced the reliability of model evaluation. The work focused on strengthening the stability of training workflows, enabling more repeatable and trustworthy results for machine learning practitioners working with advanced model training pipelines.