Worked on the NVIDIA/NeMo-Aligner repository to implement support for the Direct Preference Optimization (DPO) data format within the SFT training pipeline. This involved updating configuration management to leverage an override API, allowing for easier customization and experimentation with DPO-based workflows. Modified both training scripts and YAML configuration files to robustly process DPO data, enhancing the pipeline’s flexibility and compatibility for model training tasks. The work focused on API integration, data formatting, and configuration management using Python and YAML, ultimately reducing setup friction for DPO experiments and enabling more adaptable SFT workflows without introducing new bugs during the development period.