Direct Preference Optimization (DPO) is a simpler and more efficient alternative to traditional Reinforcement Learning from Human Feedback (RLHF) for fine-tuning large language models (LLMs) to align with human preferences. DPO uses contrastive learning to directly optimize the model using preference data, eliminating the need for reinforcement learning techniques like PPO. This approach makes training far more stable and efficient compared to RLHF, requiring no reward model or complex reward functions, reducing computational overhead, and minimizing hyperparameter tuning. DPO can be applied across various NLP tasks such as improving AI chatbots, filtering content, personalized AI assistants, and customer support automation, offering a practical solution for model fine-tuning that better reflects human values and preferences.