Worked on the huggingface/trl repository to optimize GRPO training by introducing conditional loading of the reference model, ensuring it is only loaded when the KL divergence term is required. This approach reduced memory usage and improved training speed, making experiments more cost-efficient and scalable. The solution was implemented in Python and reinforced with unit testing to verify the new behavior. The developer also refactored the core GRPO training code to enhance readability and maintainability, focusing on code quality and workflow efficiency. Their work demonstrated skills in code refactoring, model optimization, and reinforcement learning, with an emphasis on practical machine learning engineering.