What is RLHF?
RLHF (Reinforcement Learning from Human Feedback) is a training technique that aligns language models with human preferences and values by incorporating human feedback into the learning process through reinforcement learning. Rather than training solely on next-token prediction, RLHF trains models to generate outputs that humans prefer, find helpful, or judge as high quality. This approach has been crucial in creating models that are not just fluent but also helpful, harmless, and aligned with user intentions.
The RLHF process typically involves three stages: supervised fine-tuning on high-quality demonstrations to get a baseline model, collecting comparison data where humans rank or rate different model outputs for the same prompts, training a reward model to predict human preferences from this comparison data, and using reinforcement learning (often PPO - Proximal Policy Optimization) to fine-tune the language model to maximize the reward model's predictions. The result is a model that generates outputs more aligned with human preferences.
RLHF has been instrumental in developing modern AI assistants like ChatGPT, Claude, and others. It helps reduce harmful outputs, improve instruction following, increase helpfulness, and align model behavior with complex human values that are difficult to specify through rules or prompts alone. While RLHF has proven highly effective, it faces challenges including the cost of collecting human feedback, potential biases in human preferences, and difficulty scaling feedback collection. Research continues on improving RLHF methods and developing alternative alignment techniques.