Worked on enhancing the stability and reliability of the NVIDIA/Megatron-LM repository by addressing a critical bug in the Rerun workflow. Focused on backend development using Python, the work involved refining error handling and logging mechanisms to prevent crashes during checkpoint operations when transient NaN or Inf states occurred. By implementing logic to halt checkpoint saving under these edge conditions, the changes improved the robustness of long-running training jobs and reduced downtime caused by checkpoint-induced failures. This targeted fix contributed to more reliable model training and better observability, reflecting a thoughtful approach to maintaining complex machine learning infrastructure.