Reinforcement Learning from Human Feedback (RLHF) is a machine learning training technique in which human evaluators assess the outputs of an AI model, and those assessments are used to steer the model toward producing more accurate, helpful, and appropriate responses over time.
To understand RLHF, it helps to first understand the problem it solves. Large language models like GPT are initially trained on vast amounts of text data, which teaches them to predict plausible language patterns. However, predicting plausible text is not the same as producing genuinely useful, safe, or well-reasoned answers. A model trained purely on raw data may generate confident-sounding but incorrect information, or respond in ways that are technically coherent yet unhelpful in practice. RLHF was developed to close this gap between statistical fluency and real-world quality.
The process typically unfolds in three stages. First, the base model is used to generate multiple responses to a given prompt. Second, human raters - often called annotators or labelers - compare these responses and rank them according to quality criteria such as accuracy, helpfulness, and tone. Third, these human rankings are used to train a separate model called a reward model, which learns to predict which outputs humans would prefer. The original language model is then refined using reinforcement learning, an optimization approach in which the model is rewarded for generating outputs the reward model scores highly and penalized for outputs it scores poorly.
This process is closely related to fine-tuning, in that both techniques adapt a pre-trained model for more specific or higher-quality behavior. The key distinction is that fine-tuning typically relies on curated example data, while RLHF uses comparative human judgments to shape model behavior dynamically through a feedback loop.
RLHF gained widespread recognition as a core component of InstructGPT and subsequent versions of ChatGPT, where it proved instrumental in making model outputs feel more aligned with human intent. The technique is also sometimes referred to as reinforcement learning from human preferences, reflecting the fact that the signal comes not from objective correctness but from subjective human evaluation.
One important limitation of RLHF is that it inherits the biases and blind spots of its human raters. If annotators consistently favor a certain style of response, or share cultural assumptions that do not generalize broadly, the model will learn to reflect those tendencies. This makes the selection and diversity of human evaluators a significant factor in the overall quality of the resulting model. Researchers continue to explore variations such as RLAIF (Reinforcement Learning from AI Feedback), which uses AI-generated evaluations to reduce reliance on human annotation at scale.
For developers and product teams working with AI-powered tools, understanding RLHF provides useful context for why modern language models behave differently from earlier generation systems, and why the quality of training feedback has a direct influence on model reliability.