What Is RLHF?
Reinforcement Learning from Human Feedback (RLHF) is a training technique used to align language models with human values and preferences. After initial pre-training on text data, RLHF uses human judgments to teach models to produce outputs that are helpful, accurate, and safe. This process is a key reason modern AI assistants can follow instructions, avoid harmful content, and provide useful responses.
Variants and Evolution
The RLHF process typically involves three stages: supervised fine-tuning on high-quality demonstrations, training a reward model on human preference comparisons (where annotators rank multiple model outputs), and optimizing the language model against the reward model using reinforcement learning algorithms such as Proximal Policy Optimization (PPO).
Enterprise Relevance
The field has evolved beyond classical RLHF. Direct Preference Optimization (DPO) simplifies the process by eliminating the separate reward model, directly optimizing from preference pairs. Constitutional AI (CAI) uses AI-generated feedback guided by written principles. Reinforcement Learning from AI Feedback (RLAIF) uses stronger models to provide training signals, reducing the need for expensive human annotation.