This paper introduces ODPO, a generalization of Direct Preference Optimization (DPO) for aligning large language models with human preferences. DPO is a successful fine-tuning strategy that aligns language models with human preferences without requiring a reward model or reinforcement learning. However, DPO treats all preference pairs equally, which may not be optimal when the difference in quality between responses varies. ODPO addresses this by incorporating an offset that reflects the difference in quality between preferred and dispreferred responses. The offset is determined based on the extent to which one response is preferred over another. ODPO requires the difference between the likelihood of the preferred and dispreferred response to be greater than an offset value. This offset is determined based on the extent to which one response is preferred over another. Experiments on various tasks show that ODPO significantly outperforms DPO in aligning language models, especially when the number of preference pairs is limited. ODPO is more effective in generating responses with positive sentiment and reducing toxicity compared to DPO. It also performs better in summarization tasks, achieving higher win rates against human-written summaries. ODPO is a generalization of DPO that incorporates the difference between responses when modeling preference data. The intuition behind ODPO is that it requires the language model to increase the likelihood of the preferred responses compared to the dispreferred responses by an offset that is determined based on the difference between their associated reward values. When the offset is set to zero, ODPO is equivalent to DPO. The paper also discusses the connection between ODPO and softmax margin, and shows how ODPO can be used to improve the alignment of language models with human preferences. The experiments demonstrate that ODPO outperforms DPO in various tasks, including sentiment control, toxicity control, and summarization. The results show that ODPO is more effective in generating responses with positive sentiment and reducing toxicity, and achieves higher win rates against human-written summaries. The paper also discusses the limitations of ODPO, including the need for human preference data and the potential for bias in reward functions. The ethical considerations of using ODPO for aligning language models with human preferences are also discussed.This paper introduces ODPO, a generalization of Direct Preference Optimization (DPO) for aligning large language models with human preferences. DPO is a successful fine-tuning strategy that aligns language models with human preferences without requiring a reward model or reinforcement learning. However, DPO treats all preference pairs equally, which may not be optimal when the difference in quality between responses varies. ODPO addresses this by incorporating an offset that reflects the difference in quality between preferred and dispreferred responses. The offset is determined based on the extent to which one response is preferred over another. ODPO requires the difference between the likelihood of the preferred and dispreferred response to be greater than an offset value. This offset is determined based on the extent to which one response is preferred over another. Experiments on various tasks show that ODPO significantly outperforms DPO in aligning language models, especially when the number of preference pairs is limited. ODPO is more effective in generating responses with positive sentiment and reducing toxicity compared to DPO. It also performs better in summarization tasks, achieving higher win rates against human-written summaries. ODPO is a generalization of DPO that incorporates the difference between responses when modeling preference data. The intuition behind ODPO is that it requires the language model to increase the likelihood of the preferred responses compared to the dispreferred responses by an offset that is determined based on the difference between their associated reward values. When the offset is set to zero, ODPO is equivalent to DPO. The paper also discusses the connection between ODPO and softmax margin, and shows how ODPO can be used to improve the alignment of language models with human preferences. The experiments demonstrate that ODPO outperforms DPO in various tasks, including sentiment control, toxicity control, and summarization. The results show that ODPO is more effective in generating responses with positive sentiment and reducing toxicity, and achieves higher win rates against human-written summaries. The paper also discusses the limitations of ODPO, including the need for human preference data and the potential for bias in reward functions. The ethical considerations of using ODPO for aligning language models with human preferences are also discussed.