During October 2025, Damoon Shah developed a configurable bypass option for advantage estimation in the PrimeIntellect-ai/prime-rl repository. By allowing the advantage_config parameter to be set to None, Damoon enabled users to skip unnecessary advantage computations, streamlining reinforcement learning workflows and reducing computational overhead. This approach improved experimentation speed and cost efficiency, aligning with the needs of iterative RL research. The work focused on enhancing the configurability and maintainability of the estimation pipeline, leveraging Python and configuration management skills. Although the contribution was focused on a single feature, it addressed a practical need for flexibility in RL model development.