Worked on the NVIDIA/NeMo repository to address a critical bug in the GRPO frame-stacked model workflow, focusing on audio codebook handling. Applied expertise in Python, audio processing, and deep learning to correct the loss calculation and introduce padding for audio codes, which improved both model accuracy and stability in production-style workloads. Enhanced code quality and maintainability by reformatting with isort and black, ensuring readability and auditability for future contributors. Collaborated through co-authored commits and comprehensive documentation, delivering a targeted fix that reduced downstream debugging and contributed to more robust machine learning pipelines within the audio processing domain.