Worked on stabilizing the training workflow in the Modalities/modalities repository by addressing a checkpoint integrity issue within the TrainingProgress save process. Using Python and leveraging dataclasses, the developer implemented a solution that clones the TrainingProgress object before persisting, preventing mutation or overwrite of previously saved checkpoints. This approach improved data integrity and reduced the risk of checkpoint corruption, ensuring safer experiment state management and more reliable machine learning experiment reproducibility. The work focused on debugging and testing, with no new features released, but delivered enhanced reliability for experiment workflows and facilitated future audits and rollbacks through descriptive, traceable commit messages.