During October 2025, this developer enhanced reinforcement learning stability in the inclusionAI/AReaL repository by implementing the M2PO algorithm using Python. Their work focused on reducing variance in off-policy training by introducing a dedicated loss function to constrain the second moment of importance weights, and updating the loss mask logic to support these changes. Collaborating with Gemini guidance, they incorporated recommendations to further strengthen robustness. The technical approach demonstrated depth in algorithm development and loss function design, addressing the challenge of reliable policy updates for deployment. The work reflected a strong understanding of reinforcement learning and collaborative, Git-based workflows.