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 feature enables more adaptive EMA decay schedules, improving training stability and reducing the need for manual hyperparameter tuning. Andrey focused on backward-compatible API design and seamless integration with existing training flows, which supports reproducible experiments and maintainable code. His work demonstrated depth in Python, machine learning, and testing, addressing reproducibility and extensibility in model training.