Alexandr Byzov enhanced distributed training reliability for the Lightning-AI/pytorch-lightning repository by implementing compatibility for XLA distributed training parameter retrieval. He addressed the challenge of supporting both current and future torch_xla versions by conditionally leveraging torch_xla.runtime, ensuring seamless operation across evolving environments. Using Python and his expertise in distributed systems and PyTorch, Alexandr expanded the test suite to verify correct retrieval of global_ordinal, local_ordinal, and world_size parameters under XLA. This work reduced setup friction for users running distributed training on TPU-backed hardware, demonstrating a thoughtful approach to maintainability and robustness in machine learning infrastructure within a short timeframe.