In July 2025, Olga Gerasimova delivered a performance optimization for the AveragedModel component in the pytorch/pytorch repository, focusing on reducing per-iteration overhead in large-scale model training. She restructured the update_parameters method by moving the n_averaged check outside the main loop, which streamlined execution and improved resource efficiency. Her work also included integrating stochastic weight averaging (SWA) and avoiding unnecessary stream synchronization, further accelerating training cycles. Using Python and leveraging her skills in machine learning and performance profiling, Olga addressed a core bottleneck in AveragedModel workflows, demonstrating a strong understanding of low-level optimization and maintainability in production code.