During July 2025, Andrey Belykher enhanced the apple/axlearn repository by developing a flexible exponential moving average (EMA) schedule for machine learning training workflows. He introduced a step_offset parameter to the ema_schedule, allowing the initial training step for warm-up to be adjusted dynamically. This Python-based solution improved training stability and reduced the need for manual hyperparameter tuning, supporting more reproducible experiments across diverse workloads. Andrey focused on backward-compatible API design and seamless integration with existing training flows, demonstrating skills in Python, machine learning, and testing. The work addressed maintainability and extensibility of EMA scheduling, reflecting thoughtful engineering depth.