Linus Gao developed 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. He introduced a new Sample class to store transitions with associated weights and implemented mechanisms for updating and drawing prioritized batches, enabling more effective experience replay. Linus also added save and load options to support persistent buffer usage across long-running experiments, enhancing reproducibility. Working primarily in Python and leveraging skills in algorithm design and data structures, his contribution addressed the need for efficient data handling in RL pipelines and demonstrated thoughtful engineering depth within the project.