
The author introduces a novel reference-free monolithic preference alignment method, ORPO, emphasizing the importance of supervised fine-tuning (SFT) in preference alignment. ORPO outperforms other methods across various scales and demonstrates efficiency and effectiveness in aligning language models.


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reference-free-monolithic-preference-optimization-with-odds-ratio


Reference-free Monolithic Preference Optimization with Odds Ratio


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ORPO introduces a novel reference-free preference optimization method, showcasing superior performance in language model alignment.


reference-free-monolithic-preference-optimization-with-odds-ratio-a-detailed-analysis


Reference-free Monolithic Preference Optimization with Odds Ratio: A Detailed Analysis
