Nkovela developed a reinforcement learning example script setup for the Gemma3 model within the google/tunix repository, focusing on enabling rapid experimentation and benchmarking. Using Python and bash scripting, Nkovela implemented modular, reusable templates that support multiple model sizes and standardized training and evaluation parameters. This approach reduced configuration overhead and improved reproducibility, allowing data scientists and researchers to quickly set up and run RL experiments. The work established a foundation for future reinforcement learning tasks and performance comparisons in Tunix, aligning with product goals to accelerate model evaluation cycles and streamline workflows for machine learning experimentation and benchmarking.