In May 2025, Jerome developed state return support for the PettingZooWrapper in the pytorch/rl repository, focusing on environment integration and reinforcement learning workflows. He implemented functionality to capture and propagate environment state within the training loop, exposing this state through the output TensorDict. Using Python, Jerome ensured that the new feature improved reproducibility and facilitated stateful debugging and analysis of RL experiments. He also created comprehensive tests to verify state capture, strengthening test coverage and reliability. The work demonstrated a deep understanding of environment wrappers and testing, addressing reproducibility challenges in reinforcement learning without introducing unnecessary complexity.