Sam developed a scalable distributed training workflow for Direct Preference Optimization (DPO) with Llama 3.1 in the pytorch/torchtune repository, focusing on enabling efficient multi-node fine-tuning of large language models. Leveraging deep learning and distributed systems expertise, Sam implemented a distributed DPO training recipe using PyTorch, which improved throughput and resource utilization for large-scale experiments. The work involved integrating DPO pipelines and adjusting the training infrastructure to support scalable experimentation, laying a foundation for faster iteration cycles. Over the month, Sam’s contributions demonstrated depth in distributed machine learning engineering, though the focus remained on feature development rather than bug resolution.