Worked on stabilizing deep learning model training in the microsoft/dion repository by addressing a critical issue that caused NaN values during optimization. Using Python and leveraging expertise in machine learning, implemented a guard within the normuon_normalization routine to clamp the norm_U_new variable to a minimum threshold when gradients were zero. This approach prevented division by zero errors, particularly in scenarios with zero-initialized weights, and ensured reliable, repeatable loss behavior across different initialization schemes. The work focused on improving training stability rather than adding new features, ultimately reducing debugging time and interruptions for models utilizing zero-initialized output projections.