RL
Project Overview: Reinforcement Learning from Human Feedback (RLHF) Data Annotation In RLHF projects, I contribute to the critical process of aligning AI model behavior with human values and preferences. My work directly impacts how models learn to distinguish between good, better, and best responses. Key aspects of my RLHF experience include: Preference Ranking: Comparing multiple model-generated responses and ranking them according to predefined criteria such as relevance, helpfulness, safety, and factual accuracy. Reward Modeling Support: Providing high-quality human preference data that serves as the foundation for training reward models, which in turn guide the reinforcement learning process. Edge Case Identification: Recognizing and flagging ambiguous, biased, or potentially harmful responses to help models avoid generating undesirable content. Consistency Calibration: Ensuring that preference judgments remain consistent across similar prompts, helping models develop stable and