During January 2026, Auraithm developed a new reward scaling strategy called GDPO for the modelscope/ms-swift repository, focusing on multi-reward optimization in reinforcement learning. The approach addressed challenges in normalizing and aggregating multiple reward signals, enabling more stable and interpretable training outcomes. Auraithm implemented the solution using Python, leveraging expertise in data normalization and machine learning to integrate the strategy directly into the codebase. While the contribution was limited to a single feature over one month, the work demonstrated a targeted and technically sound solution to a nuanced problem in reinforcement learning reward design, reflecting depth in both implementation and domain understanding.