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 supported cost-efficient model iteration, aligning with modern RL research needs. The work focused on configuration management and was implemented in Python, enhancing both the flexibility and maintainability of the estimation pipeline. While the contribution addressed a targeted feature, it demonstrated thoughtful engineering to optimize resource usage in RL environments.