While recent preference alignment algorithms for language models have demonstrated promising results, supervised fine-tuning (SFT)  remains imperative for achieving successful convergence. In this paper, we study the crucial role of SFT within the context of preference alignment, emphasizing that a minor penalty for the disfavored generation style is sufficient for preference-aligned SFT. Building on this foundation, we introduce a straightforward and innovative reference model-free monolithic odds ratio preference optimization algorithm, ORPO, eliminating the necessity for an additional preference alignment phase. We demonstrate, both empirically and theoretically, that the odds ratio is a sensible choice for contrasting favored and disfavored styles during SFT across the diverse sizes from 125M to 7B. Specifically, fine-tuning Phi-2 (2.7B) , Llama-2 (7B) , and Mistral (7B)  with ORPO on the UltraFeedback alone surpasses the performance of state-of-the-art language models with more than 7B and 13B parameters: achieving up to 12.20% on(Figure1) , 66.19% on IFEval (instruction-level loose, Table6) , and 7.32 in MT-Bench (Figure12) . We release code111https://github.com/xfactlab/orpoand model checkpoints for Mistral-ORPO-(7B) 222https://huggingface.co/kaist-ai/mistral-orpo-alphaand Mistral-ORPO-(7B) .333https://huggingface.co/kaist-ai/mistral-orpo-beta