Worked on the OpenPipe/ART repository to enhance policy loss robustness by introducing a new epsilon_high configuration option for asymmetric clipping in policy loss calculations. The implementation, written in Python and leveraging deep learning and reinforcement learning techniques, defaults epsilon_high to epsilon when not explicitly provided or set to None, reducing the risk of misconfiguration. Refactored the retrieval logic to simplify handling of edge cases, which improved the stability and reliability of policy optimization experiments. Updated configuration defaults and documentation to better align with safe experimentation practices. No major bugs were addressed during this period, with focus placed on feature development.