ORPO is an innovative algorithm that simplifies the LLM fine-tuning process by directly integrating preference alignment into a single-step supervised fine-tuning. This approach eliminates the need for complex, multi-stage processes and extensive hyperparameter tuning typically required in traditional methods like Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO). ORPO incorporates an odds ratio-based penalty into the conventional negative log-likelihood (NLL) loss function during supervised fine-tuning (SFT), helping distinguish between favored and disfavored responses. The algorithm has demonstrated superior performance in various benchmark tasks, outperforming state-of-the-art models that use traditional fine-tuning methods, while being resource-efficient and scalable. ORPO's approach to preference alignment preserves the domain adaptation benefits of SFT while simultaneously aligning the model with user preferences, reducing the risk of overfitting specific training examples. By integrating optimal regularization and pruning, ORPO can develop models that are not only accurate but also efficient and scalable, making it a powerful way to fine-tune large language models.