Worked on improving the reliability of the training lifecycle in the Lightning-AI/pytorch-lightning repository by addressing a bug related to state carryover between training runs. Using Python and deep learning frameworks such as PyTorch, the developer implemented a fix that resets the trainer’s should_stop flag to False at the start of each fit call, preventing premature halting of subsequent training sessions. To ensure robust behavior, a regression test was added to validate EarlyStopping across multiple fits. This work enhanced the stability of iterative experimentation and continuous integration pipelines, reducing the likelihood of flaky runs and unnecessary compute resource consumption.