  As development of large language models (LLM) progresses, aligning them with
human preferences has become increasingly important. We propose stepwise DPO
(sDPO), an extension of the recently popularized direct preference optimization
(DPO) for alignment tuning. This approach involves dividing the available
preference datasets and utilizing them in a stepwise manner, rather than
employing it all at once. We demonstrate that this method facilitates the use
of more precisely aligned reference models within the DPO training framework.
Furthermore, sDPO trains the final model to be more performant, even
outperforming other popular LLMs with more parameters.
