Alexandr Byzov developed enhanced XLA distributed training parameter retrieval compatibility for the Lightning-AI/pytorch-lightning repository, focusing on improving reliability for distributed training on TPU and XLA-backed environments. He implemented logic in Python to retrieve global_ordinal, local_ordinal, and world_size parameters under XLA, ensuring correct propagation in distributed setups. By conditionally leveraging torch_xla.runtime when available, Alexandr maintained compatibility with newer torch_xla versions and prevented breakages in updated environments. He also expanded the test suite to verify the new behavior across supported configurations. This work reduced setup friction and aligned with robust, high-performance distributed training goals in machine learning workflows.