Worked on the pytorch/torchrec repository to address stability concerns in distributed training workflows. Focused on Python-based machine learning development, the work involved reverting the set_optimizer_step API addition in the OptimizerWrapper, restoring the previous step count propagation mechanism. This rollback was carefully implemented to maintain backward compatibility and ensure consistent optimizer step semantics, reducing the risk of training divergence and downstream issues. The change was thoroughly documented to clarify the rationale and prevent future regressions. By minimizing the surface area of modifications, the update stabilized torchrec’s training processes without introducing new features, reflecting a methodical approach to software maintenance.