RLHF (Reinforcement Learning from Human Feedback)

RLHF is a training technique that uses human feedback to fine-tune AI models to be more aligned with human preferences.

Process: 1. Supervised Fine-tuning: Train on human demonstrations 2. Reward Model: Train a model to predict human preferences 3. RL Optimization: Use reward model to guide further training

Why RLHF Matters: - Makes models more helpful - Reduces harmful outputs - Improves instruction following - Aligns AI with human values

Used By: - ChatGPT/GPT-4 - Claude - Gemini - Most modern chatbots

Limitations: - Expensive (requires human labelers) - Can reduce model capabilities - Subjective preferences vary