تحسين تفضيلات نسبة الأرجحية

Odds Ratio Preference Optimization starts with supervised fine-tuning on preferred responses and adds a penalty that contrasts the policy’s odds for chosen and disfavored responses. This monolithic objective avoids a separate reference model and a distinct preference-optimization phase. The paper describes the objective as a way to apply preference alignment during SFT; it does not mean that every data pipeline or implementation uses no auxiliary components. The odds transformation produces a different penalty shape from a raw log-probability difference. Outcomes depend on the preference dataset, coefficient, base model, and evaluation. ORPO is a training method, not an inference-time decoding rule or a guarantee that preferences are faithfully represented.