--- language: en title: "RLHF" description: "RLHF uses human evaluations to guide model training. Learn about the role of preferences and the limitations of evaluating AI responses." canonical: "https://tropk.ai/en/resources/glossary/rlhf" last_modified: "2026-10-12T16:13:24.260Z" --- # RLHF Reviewed on 2026-10-06. ## What it means RLHF stands for Reinforcement Learning from Human Feedback. It is a training approach that uses people’s evaluations to guide a model’s behavior. In an implementation studied in InstructGPT, comparisons between responses train a reward model, which then guides the text generator’s [fine-tuning](/en/resources/glossary/fine-tuning). ## Why it matters for marketing A well-presented response may reflect learned preferences for helpfulness and style without being factually correct in every respect. For marketing teams, this means assessing clarity, suitability, and support for claims separately. Subjective approval of a text does not replace checking a commercial term, a source, or a cited figure. ## Example in practice Consider a subscription platform that trains an assistant to explain cancellations. Evaluators prefer polite responses but reject those that promise refunds outside the policy. In the human comparisons used by the RLHF project, this difference needs to be part of the preference criteria. The team decides to value courtesy alongside adherence to the rules, and tests cases that were not used in training. ## How to apply or verify When evaluating a solution that says it uses RLHF, look for a description of the process: who evaluated it, what instructions they received, and which behaviors were prioritized. Prepare questions with verifiable answers and situations where people may disagree. Record preference and factual accuracy in separate fields, since improvement in the first measure does not prove an equivalent improvement in the second. ## Important distinctions RLHF does not mean that a person reviews every response during use. Human work provides signals for a training stage. It is also not synonymous with all fine-tuning or every alignment technique. The InstructGPT study acknowledges remaining errors and links behavior to the preferences of the groups involved, without representing universal human values. ## Sources and references - [Training language models to follow instructions with human feedback]() — Ouyang e colaboradores / arXiv ## Continue a leitura - [AI hallucination]() - [Fine-tuning]() - [LLM]() ## About the author [Vinicius Pacheco]() Responsible for Tropk’s technical content, focusing on crawling, indexing, structured data, and agent access to content. --- [All pages in Markdown]()