During October 2024, Hlin focused on stabilizing distributed training workflows in the pytorch/torchrec repository by addressing a regression in optimizer step propagation. Using Python and leveraging expertise in machine learning and software development, Hlin reverted the set_optimizer_step API addition in the OptimizerWrapper, restoring the previous step count propagation behavior. This rollback ensured backward compatibility and consistent optimizer semantics, reducing the risk of training divergence and downstream breakages. The work was carefully scoped to minimize changes, with thorough documentation provided to prevent future regressions. Hlin’s contribution demonstrated depth in maintaining stability and reliability in complex distributed machine learning systems.