This paper investigates the learning dynamics of Direct Preference Optimization (DPO) in aligning large language models (LLMs) with human preferences. The authors provide a theoretical analysis of how the distribution of preference datasets influences the rate of model updates and offer rigorous guarantees on training accuracy. They show that higher preference distinguishability leads to faster weight parameter updates and a more rapid decrease in loss. Theoretical insights reveal that DPO is prone to prioritizing behaviors with higher distinguishability, potentially deprioritizing less distinguishable yet crucial ones. Empirical validation on contemporary LLMs and alignment tasks confirms these findings, highlighting the vulnerability of DPO-trained models to misalignment. The study also shows that aligned models can be more susceptible to misalignment training due to the separability of positive and negative examples. The results emphasize the importance of considering preference or behavior prioritization in alignment training. The paper contributes new theoretical guarantees on the accuracy of models trained with DPO and provides insights into how distributional properties affect model behavior. The findings have practical implications for future alignment approaches and highlight the need for developing advanced methods to ensure safer and beneficial models.This paper investigates the learning dynamics of Direct Preference Optimization (DPO) in aligning large language models (LLMs) with human preferences. The authors provide a theoretical analysis of how the distribution of preference datasets influences the rate of model updates and offer rigorous guarantees on training accuracy. They show that higher preference distinguishability leads to faster weight parameter updates and a more rapid decrease in loss. Theoretical insights reveal that DPO is prone to prioritizing behaviors with higher distinguishability, potentially deprioritizing less distinguishable yet crucial ones. Empirical validation on contemporary LLMs and alignment tasks confirms these findings, highlighting the vulnerability of DPO-trained models to misalignment. The study also shows that aligned models can be more susceptible to misalignment training due to the separability of positive and negative examples. The results emphasize the importance of considering preference or behavior prioritization in alignment training. The paper contributes new theoretical guarantees on the accuracy of models trained with DPO and provides insights into how distributional properties affect model behavior. The findings have practical implications for future alignment approaches and highlight the need for developing advanced methods to ensure safer and beneficial models.