Worked on the huggingface/trl repository to address a stability issue in the GRPOTrainer component, focusing on improving the reliability of entropy threshold calculations during training. Identified and resolved a runtime type error by ensuring quantile inputs were cast to float before being processed by torch.quantile, which eliminated intermittent failures caused by dtype mismatches. This targeted bug fix enhanced the robustness of production training pipelines and reduced debugging overhead for entropy-based controls. The work demonstrated proficiency in Python, PyTorch, and deep learning workflows, with clear commit messaging and traceability to support maintainability and consistent performance in machine learning environments.