Adithya R. developed support for the Direct Preference Optimization (DPO) data format within the NVIDIA/NeMo-Aligner SFT training pipeline, focusing on enhancing flexibility and compatibility for DPO-based experiments. By updating configuration files to leverage an override API and modifying training scripts, Adithya enabled seamless processing of DPO data, reducing setup friction for users. The work involved Python and YAML for both configuration management and data formatting, integrating API-driven customization into the workflow. This feature addressed the need for more adaptable SFT pipelines, demonstrating depth in model training and configuration, though the scope was limited to a single feature over one month.