The Hitchhiker's Guide to Human Alignment with *PO

With the growing utilization of large language models (LLMs) across domains, alignment towards human preferences has become one of the most critical aspects of training models. At the forefront of state-of-the-art <PRE_TAG>human alignment</POST_TAG> methods are preference optimization methods (*PO). However, prior research has often concentrated on identifying the best-performing method, typically involving a grid search over <PRE_TAG>hyperparameters</POST_TAG>, which can be impractical for general practitioners. In this paper, we aim to identify the algorithm that, while being performant, is simultaneously more robust to varying <PRE_TAG>hyperparameters</POST_TAG>, thereby increasing the likelihood of achieving better results. We focus on a realistic <PRE_TAG>out-of-distribution (OOD) scenario</POST_TAG> that mirrors real-world applications of <PRE_TAG>human alignment</POST_TAG>, offering practical insights into the strengths and weaknesses of these methods. Furthermore, to better understand the shortcomings of generations from the different methods, we analyze the model generations through the lens of <PRE_TAG>KL divergence</POST_TAG> of the SFT model and the response length statistics. Our analysis reveals that the widely adopted DPO method consistently produces lengthy responses of inferior quality that are very close to the SFT responses. Motivated by these findings, we propose an embarrassingly simple extension to the DPO algorithm, LN-DPO, resulting in more concise responses without sacrificing quality compared to the policy obtained by vanilla DPO.