Worked on the NVIDIA/NeMo-RL repository to improve the stability and reliability of the language model integration pipeline, focusing on backend development and natural language processing using Python. Addressed a critical bug in the Language Model Message Formatting Loop, ensuring that message formatting accurately reflects preceding log messages. This fix improved the accuracy of prompt construction for conversational reinforcement learning scenarios and reduced formatting-related failures in production. The solution involved targeted debugging within llm_message_utils.py and introduced commit-level traceability for future audits. The work demonstrated a methodical approach to enhancing messaging workflows, directly impacting the reliability of downstream language model tasks.