Linus Gao developed a prioritized replay functionality for the ReplayBuffer in the WE-Autopilot/Red-Team repository, focusing on improving sample efficiency and training stability in reinforcement learning workflows. Using Python and leveraging skills in algorithm design and data structures, Linus introduced a new Sample class to store transitions with associated weights and implemented mechanisms for updating and drawing prioritized batches. The work also included adding save and load options to support persistent buffer usage across experiments, directly addressing the need for reproducibility and long-running training. This feature enhanced data efficiency and experimentation throughput, reflecting a thoughtful and targeted engineering approach.