encyclopedia
RLHF is the primary technique behind making raw language models into useful assistants. Human annotators rate pairs of model outputs, and those preferences are used to train a "reward model" that scores responses. The language model is then fine-tuned with reinforcement learning to maximize this reward, resulting in an assistant that is more aligned with human values. ChatGPT, Claude, and Gemini all rely on variants of RLHF.
A training technique that uses human preference ratings to fine-tune AI models to produce more helpful, accurate, and safe responses.
We help teams go from definitions to deployed workflows—safely and fast.
RLHF typically involves three phases applied on top of a pre-trained foundation model.
Because human annotation is expensive and slow, researchers have developed alternatives. RLAIF (Reinforcement Learning from AI Feedback) uses a larger "critic" model to provide preference labels instead of humans. Direct Preference Optimization (DPO) removes the separate reward model step, directly optimizing the language model from preference data, which is simpler and often equally effective.
Over-optimization of the reward model can cause "reward hacking," where the model learns to produce responses that score well on the reward metric but are not actually better. This tradeoff between helpfulness and safety requires careful calibration.
Constitutional AI (CAI) is Anthropic's variant. Instead of relying solely on human preference labels, CAI uses a set of written principles (a "constitution") to guide an AI critic model to generate preference data, reducing reliance on human annotators for safety-related labeling.
Get a copy of “Reinforcement Learning from Human Feedback (RLHF)” and an AI readiness checklist in your inbox.