Developed a configurable bypass option for advantage estimation in the PrimeIntellect-ai/prime-rl repository, enabling users to set advantage_config to None and skip unnecessary computations during reinforcement learning workflows. This feature was implemented using Python and focused on configuration management to enhance flexibility and maintainability within the RL estimation pipeline. By allowing selective omission of advantage estimation, the update reduced computational overhead and accelerated experimentation, supporting cost efficiency and faster iteration cycles. The work demonstrated a targeted approach to optimizing RL processes, leveraging skills in Python and reinforcement learning to deliver a more adaptable and resource-conscious configuration for the project.