RLHF: The Key to Better LLMs
How Reinforcement Learning from Human Feedback transforms language model capabilities.
Reinforcement Learning from Human Feedback (RLHF) has emerged as one of the most important techniques for aligning large language models with human preferences and values. This approach has been instrumental in making models like GPT-4 and Claude more helpful, harmless, and honest.
The RLHF process typically involves three main stages: supervised fine-tuning, reward model training, and policy optimization. Each stage builds upon the previous one, gradually shaping the model's behavior to better align with human expectations.
In the supervised fine-tuning stage, the model is trained on high-quality demonstrations of desired behavior. This creates a foundation for the model to understand what good responses look like. The quality and diversity of this training data is crucial for downstream performance.
The reward model is trained on human preference data - comparisons between different model outputs. Humans evaluate which response is better, and this feedback is used to train a model that can predict human preferences. This reward model then guides the final optimization stage.
Policy optimization uses reinforcement learning algorithms like PPO to fine-tune the language model. The reward model provides signals about what responses are preferred, and the language model learns to generate outputs that maximize this reward while maintaining its general capabilities.