RLHF (Reinforcement Learning from Human Feedback)
What is RLHF (Reinforcement Learning from Human Feedback)?
RLHF is a training method where AI models learn to produce better responses by getting feedback from humans. Instead of just learning from data, the model gets rated on its answers and adjusts to give more helpful, accurate, and safe responses. This makes AI systems more useful and aligned with human values.
Technical Details
RLHF typically combines supervised fine-tuning with reinforcement learning, using human preference data to train a reward model that guides policy optimization. Common implementations use Proximal Policy Optimization (PPO) to fine-tune language models while maintaining stability during training.
Real-World Example
ChatGPT uses RLHF extensively - human trainers rank different responses, and the model learns to generate more helpful and appropriate answers based on this feedback, making conversations more natural and useful.
AI Tools That Use RLHF (Reinforcement Learning from Human Feedback)
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