DPO vs RLHF: How Human Feedback Shapes AI Responses

AI models like ChatGPT and Gemini are fine-tuned using human preferences to produce responses that people find helpful and appropriate. Two key techniques behind this process are Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF). DPO trains a model directly on paired examples of preferred and rejected answers, teaching it to favor responses similar to human-chosen ones. RLHF follows a multi-stage pipeline that includes instruction fine-tuning, human preference collection, and training a separate reward model to score outputs. Both methods depend heavily on the quality of human feedback, meaning biased or inconsistent judgments can negatively influence the model's behavior.