In December 2025, Juan Canta developed and integrated a bias-corrected KL estimator for the GRPO algorithm within the huggingface/trl repository, focusing on improving reinforcement learning workflows for large language model training. Using Python and leveraging his expertise in machine learning and reinforcement learning, he addressed estimator bias to enable more reliable KL divergence calculations, which are critical for model stability and performance. Juan collaborated closely on code integration and updated configuration parameters and documentation to ensure seamless adoption. Comprehensive tests were added to validate the new estimator, enhancing CI coverage and reducing regression risk across existing deployment pipelines.