ORPO: Monolithic Preference Optimization without Reference Model
<|reference_start|>ORPO: Monolithic Preference Optimization without Reference Model: 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 $\text{AlpacaEval}_{2.0}$ (Figure 1), 66.19% on IFEval (instruction-level loose, Table 6), and 7.32 in MT-Bench (Figure 12). We release code and model checkpoints for Mistral-ORPO-$\alpha$ (7B) and Mistral-ORPO-$\beta$ (7B).<|reference_end|>
@article{hong2024orpo:,
    title={ORPO: Monolithic Preference Optimization without Reference Model},
    author={Jiwoo Hong, Noah Lee, James Thorne},
    journal={arXiv preprint arXiv:2403.07691},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07691},
    primaryClass={cs.CL cs.AI}
}