November 2025 (google/tunix) focused on delivering business value through direct human-alignment in training workflows. Implemented Odds Ratio Preference Optimization (ORPO) training support, enabling alignment with human preferences within the training loop without relying on a separate reference model. Added ORPO-specific tests and updated loss functions to support the new algorithm, driving memory efficiency and training performance improvements. No major bugs reported this month. Impact includes reduced compute costs, faster experimentation cycles, and a more scalable, production-ready training pipeline. Technologies demonstrated include Python-based ML framework development, loss-function engineering, test automation, and performance optimization.