Developed cross-framework checkpoint persistence for the pytorch/xla repository, enabling seamless save and load of training states with compatibility between PyTorch and JAX. Designed and implemented a unified checkpoint API that supports conversion between PyTorch-style and JAX-style formats, allowing users to migrate and experiment across deep learning ecosystems with reduced friction. The solution included new save and load functions, comprehensive unit tests to ensure correctness and robustness, and addressed edge cases in checkpointing workflows. Leveraged Python and YAML to build and validate the feature, focusing on checkpointing, deep learning, and machine learning workflows to improve training resilience and flexibility.