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        <title>ORPO: Monolithic Preference Optimization without Reference Model</title>
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        <namePart type="given">Jiwoo</namePart>
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            <namePart type="given">Yun-Nung</namePart>
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    <abstract>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 revisit SFT in the context of preference alignment, emphasizing that a minor penalty for the disfavored style is sufficient for preference alignment. Building on this foundation, we introduce a straightforward reference model-free monolithic odds ratio preference optimization algorithm, ORPO, eliminating the need 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 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 including Llama-2 Chat and Zephyr with more than 7B and 13B parameters: achieving up to 12.20% on AlpacaEval 2.0 (Figure 1), and 7.32 in MT-Bench (Table 2). We release code and model checkpoints for Mistral-ORPO-α (7B) and Mistral-ORPO-β (7B).</abstract>
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        <url>https://aclanthology.org/2024.emnlp-main.626</url>
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%0 Conference Proceedings
%T ORPO: Monolithic Preference Optimization without Reference Model
%A Hong, Jiwoo
%A Lee, Noah
%A Thorne, James
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F hong-etal-2024-orpo
%X 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 revisit SFT in the context of preference alignment, emphasizing that a minor penalty for the disfavored style is sufficient for preference alignment. Building on this foundation, we introduce a straightforward reference model-free monolithic odds ratio preference optimization algorithm, ORPO, eliminating the need 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 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 including Llama-2 Chat and Zephyr with more than 7B and 13B parameters: achieving up to 12.20% on AlpacaEval 2.0 (Figure 1), and 7.32 in MT-Bench (Table 2). We release code and model checkpoints for Mistral-ORPO-α (7B) and Mistral-ORPO-β (7B).
%U https://aclanthology.org/2024.emnlp-main.626
%P 11170-11189

Markdown (Informal)

[ORPO: Monolithic Preference Optimization without Reference Model](https://aclanthology.org/2024.emnlp-main.626) (Hong et al., EMNLP 2024)

• ORPO: Monolithic Preference Optimization without Reference Model (Hong et al., EMNLP 2024)

ACL

• Jiwoo Hong, Noah Lee, and James Thorne. 2024. ORPO: Monolithic Preference Optimization without Reference Model. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11170–11189, Miami, Florida, USA. Association for Computational Linguistics.