During April 2026, Yaoruda focused on enhancing the reliability of deep learning workflows in the modelscope/ms-swift repository by addressing a critical padding synchronization issue affecting DSA models. Using Python and leveraging expertise in machine learning, Yaoruda identified and resolved inconsistencies between the template.padding_free attribute and runtime arguments after the model preparation step. This targeted bug fix improved the consistency of padding decisions during deployment, reducing the risk of errors in the model preparation pipeline. The work demonstrated careful debugging and precise code modification in a core system component, with clear documentation and traceability maintained through structured commits and pull requests.