Developed and integrated the Group Filtered Policy Optimization (GFPO) algorithm into the huggingface/trl repository, focusing on enhancing model training efficiency and output quality. The work involved implementing new trainer and configuration classes in Python, enabling end-to-end usage of GFPO for reinforcement learning workflows. Comprehensive documentation and practical usage examples were provided to support adoption by other teams. By introducing group-filtered scoring, the integration allowed for more targeted and quality-focused model completions, streamlining experimentation cycles and improving model alignment with desired objectives. The project leveraged skills in machine learning, model training, and Python development to deliver this advanced capability.