Developed a per-token loss calculation feature with granular logging for model training in the alibaba/ROLL repository, focusing on enhancing feedback and optimization at the token level. Leveraged Python programming and data analysis skills to implement mechanisms that record loss for each token during training, enabling more precise performance evaluation and targeted model improvements. The approach improved observability by updating logging systems to reflect detailed loss metrics, which facilitated more efficient debugging and analysis of machine learning workflows. All changes were traceable through linked commits, supporting robust review and rollback processes. No bug fixes were recorded during this period of focused feature development.