Worked on the AI-Hypercomputer/maxtext repository to address critical regressions affecting Mixture-of-Experts (MoE) training throughput and stability. Focused on restoring expert sharding and refining checkpoint naming, the developer implemented changes in Python using JAX and deep learning techniques. These updates resolved two independent bugs that had reduced training efficiency, resulting in a threefold throughput recovery on MI325X hardware. The work included updating activation batch logic to support sharded MoE paths and ensuring compatibility with rematerialization policies. Comprehensive documentation and tests were added to cover edge cases, improving maintainability and reducing the risk of future regressions in MoE workflows.