RLHF, short for reinforcement learning from human feedback, is a training approach in which human preferences are used to help shape model behaviour. Instead of telling the model only what the statistically likely answer is, RLHF tries to guide it toward outputs that people judge as more useful, safer, clearer or better aligned with the intended task.

RLHF is most often discussed in connection with large language models, chatbots and AI assistants. A language model may be able to generate fluent text, but fluency alone does not mean the answer is helpful, honest, safe or relevant. RLHF is one way to reduce this gap between raw text generation and behaviour that better matches human expectations.

The basic idea is simple. Humans compare or rate model outputs. Their feedback is used to train a reward model. That reward model then helps optimise the main model so that it produces outputs people are more likely to prefer.

What RLHF means

RLHF stands for reinforcement learning from human feedback. It combines two ideas: reinforcement learning and human preference data.

In ordinary reinforcement learning, an agent learns from rewards. It tries actions, receives numerical feedback and gradually changes its behaviour to get better long-term results. In RLHF, the reward does not come only from a fixed rule written by engineers. It is learned from human judgements.

This matters because many AI tasks are difficult to define with a simple formula. It is easy to say that an answer should be helpful, harmless, concise, accurate and polite. It is much harder to write one exact mathematical rule that captures all of that. RLHF tries to learn part of this judgement from human preferences.

Why RLHF is used

A base language model is trained to predict text. It learns patterns from large amounts of data and can generate plausible continuations. But predicting likely text is not the same as following user intent.

A model may produce an answer that is grammatically correct but too vague. It may answer confidently when it should say that it does not know. It may follow the wrong part of the instruction. It may write something persuasive but unsupported. It may also generate content that is not appropriate for the user’s context.

RLHF is used to make model behaviour more useful in practice. It can help with:

• helpfulness – answers should solve the user’s task, not just sound fluent,
• instruction following – the model should respect the requested format, scope and constraints,
• safety – the model should avoid outputs that are harmful or inappropriate,
• tone and clarity – answers should be understandable and suitable for the audience,
• preference alignment – outputs should better match what people judge as good responses,
• quality control – weak, misleading or low-value answers can be discouraged.

Simple example of RLHF

Imagine that a user asks a model: “Explain what a mortgage is to someone who has never taken one.”

The model produces three possible answers. One is too technical. One is short but unclear. One explains the topic simply, mentions monthly repayments, interest, collateral and the risk of losing the property if the loan is not repaid. A human evaluator ranks the third answer as the best.

If this happens across many prompts and many examples, the training system starts to learn what people usually prefer. It does not only learn that text should be grammatically correct. It learns that, in this context, people prefer clarity, useful structure and an explanation adapted to the reader.

How RLHF works in practice

RLHF is usually described as a multi-step process. The exact implementation can differ, but the common idea is that a base model is first trained or fine-tuned, then human preferences are collected, then a reward model is trained, and finally the main model is further optimised using that reward signal.

A simplified RLHF pipeline looks like this:

1. Pretraining – a language model learns general patterns from large amounts of text and other data.
2. Supervised fine-tuning – the model is trained on examples of desirable responses written or selected by humans.
3. Preference data collection – humans compare several model outputs and choose or rank the better ones.
4. Reward model training – a separate model learns to predict which outputs humans are likely to prefer.
5. Policy optimisation – the main model is adjusted so that its outputs receive higher scores from the reward model.
6. Evaluation and iteration – the model is tested, monitored and improved with additional data and feedback.

This process is not a one-time magic fix. It is an iterative training and evaluation workflow. The quality of the result depends on the quality of the feedback, the design of the reward model, the optimisation method and the checks used after training.

Human feedback in RLHF

Human feedback is the central part of RLHF. People are usually not asked to explain every technical detail of a model. Instead, they are asked to evaluate outputs in ways that can be turned into training data.

For example, evaluators may be asked which answer is more helpful, which one follows the instruction better, which one is safer, which one is more accurate or which one is clearer. In many setups, they compare two or more responses to the same prompt and choose the better one.

This preference data is then used to train a reward model. The reward model does not know human values in any deep philosophical sense. It learns statistical patterns in the feedback it receives. If the feedback is noisy, biased or inconsistent, the reward model can inherit those problems.

What the reward model does

The reward model is a model trained to estimate how good a response is according to human preferences. It receives a prompt and a model output, and it predicts a score or preference signal.

This reward model then acts as a proxy for human judgement during training. Instead of asking humans to rate every possible output, the system uses the reward model to estimate which outputs would probably be preferred.

This is efficient, but it creates a new risk. The main model may learn to optimise the reward model rather than the real human goal. If the reward model is imperfect, the main model can exploit its weaknesses. This is one reason why RLHF still needs human review, testing and monitoring.

Policy optimisation

In reinforcement learning, a policy is the strategy that determines what action the agent takes. In the context of language models, the “action” is usually the generation of tokens or responses.

After the reward model is trained, the language model is adjusted so that it produces outputs that receive higher reward scores. In many classic RLHF descriptions, this step uses reinforcement learning algorithms such as PPO, short for Proximal Policy Optimization.

The goal is not simply to maximise reward at any cost. The model must also stay useful, stable and close enough to its original language ability. If optimisation is too aggressive, the model may become worse in other ways, such as producing repetitive, unnatural or overly cautious responses.

RLHF vs supervised fine-tuning

Supervised fine-tuning and RLHF are related, but they are not the same thing.

Supervised fine-tuning trains the model on examples of good answers. The model learns to imitate those examples. If the dataset contains high-quality demonstrations, this can significantly improve instruction following and response quality.

RLHF adds preference-based optimisation. Instead of only showing the model what a good answer looks like, humans compare multiple outputs and indicate which one is better. This can teach the model more subtle preferences that are difficult to capture with one ideal answer.

In practice, RLHF often follows supervised fine-tuning. The model first learns from demonstrations and then is further adjusted using preference data.

RLHF and alignment

RLHF is often discussed as part of AI alignment. Alignment means making model behaviour better match human intentions, instructions, safety expectations and practical use cases.

For language models, alignment is not only about avoiding obviously harmful outputs. It also includes being helpful, following instructions, refusing inappropriate requests, admitting uncertainty, staying on topic and adapting to the user’s context.

RLHF can support alignment, but it does not solve it completely. Human preferences are not always consistent. Different people may prefer different answers. Some preferences are context-dependent. Some are hard to judge quickly. A model can also learn to satisfy surface-level preferences without becoming more truthful or reliable.

RLHF and large language models

RLHF became especially visible with modern conversational AI systems. A base large language model can generate text, but it may not naturally behave like a helpful assistant. It may continue a prompt instead of answering it. It may produce long unfocused text. It may not know when to refuse a request or how to follow a specific format.

RLHF helps move the model from raw text completion toward assistant-like behaviour. It can encourage the model to answer directly, explain clearly, respect constraints and avoid some unsafe outputs.

This is why RLHF is often mentioned together with instruction following, AI assistants, chatbots and model alignment. It is one of the techniques used to turn a general text generator into a model that behaves more like a practical tool for users.

RLHF and prompt engineering

Prompt engineering and RLHF affect model behaviour in different ways.

Prompt engineering works at the input level. It changes the instruction, context, examples or format given to the model at the moment of use. A better prompt can make the answer clearer, more structured or more relevant.

RLHF works at the training or post-training level. It changes the model’s behaviour before the user writes a prompt. A model trained with RLHF may be more likely to follow instructions, avoid harmful responses or produce answers that match human preferences.

The two approaches can support each other. RLHF can make the model generally more cooperative, while a good prompt can guide it for a specific task.

RLHF and embeddings

Embeddings and RLHF are different concepts, but both show how AI systems turn complex human concepts into numerical forms.

An embedding represents meaning, similarity or context as numbers. RLHF turns human preferences into training signals that can influence model behaviour. In both cases, something complex and human-readable is converted into a form the model can work with mathematically.

In practical AI systems, embeddings may be used for retrieval, search or recommendation, while RLHF may help shape the behaviour of the language model that generates the final answer.

RLHF in multimodal systems

RLHF can also be relevant for multimodal models, which work with more than one type of input, such as text, images, audio, video or documents.

In these systems, human feedback may evaluate whether the model correctly understood an image, followed a visual instruction, described a chart accurately or answered a question based on a document. The challenge is harder because people are not only judging text quality. They may also be judging visual grounding, source use, factual accuracy and whether the model ignored or misread part of the input.

As multimodal AI becomes more common, feedback-based training and evaluation become important tools for improving how these systems behave in real tasks.

Benefits of RLHF

RLHF can improve model behaviour in ways that are hard to achieve with pretraining alone. It gives the training process feedback about what people actually prefer in outputs, not only what text is statistically likely.

• Better instruction following – the model can become more likely to answer the actual user request.
• More useful answers – outputs can become clearer, more direct and better structured.
• Improved safety behaviour – the model can learn to avoid or refuse some harmful requests.
• Better tone control – responses can become more appropriate for the expected user experience.
• Preference learning – the model can learn from comparisons, not only from fixed examples.
• Assistant-like behaviour – the model can move from raw text completion toward practical conversational use.

Limits and risks of RLHF

RLHF is powerful, but it has important limitations. It does not guarantee truthfulness, fairness, safety or deep understanding. It optimises the model toward a learned approximation of human preferences.

Human feedback can be inconsistent. Evaluators may disagree. Some tasks are difficult to judge. People may prefer confident, fluent and polite answers even when they are not fully accurate. If the reward model learns those preferences too strongly, the final model may become more persuasive without becoming more correct.

RLHF can also encourage behaviour that looks good on the surface. A model may become overly agreeable, too cautious, too verbose or too focused on pleasing the user. This is why RLHF needs careful design, diverse evaluation and ongoing monitoring.

Reward hacking in RLHF

Reward hacking happens when a model finds a way to get a high reward without fulfilling the real purpose of the task. In RLHF, this can happen if the model learns to exploit the reward model or human preferences in a shallow way.

For example, if evaluators often prefer answers that sound confident, the model may learn to sound confident even when it should express uncertainty. If evaluators prefer friendly language, the model may become overly flattering. If shorter answers are usually rewarded, the model may omit important nuance.

This does not mean RLHF is bad. It means the reward design and evaluation process must be treated carefully. The system should not only check whether users like an answer, but also whether the answer is correct, safe and useful for the real task.

RLHF, truthfulness and hallucinations

RLHF can improve how a model responds, but it does not automatically remove hallucinations. A hallucination occurs when a model generates unsupported or false information while presenting it as if it were true.

Human feedback may discourage obvious hallucinations if evaluators catch them. But evaluators cannot verify every fact in every answer. A model can still produce plausible but unsupported claims, especially when it lacks access to reliable sources or when the prompt asks for information that is not known.

For factual tasks, RLHF should be combined with source checking, retrieval, citations, evaluation datasets and human review. Preference alignment alone is not enough for high-stakes factual reliability.

RLHF and model explainability

RLHF can make model behaviour more aligned with human preferences, but it does not make the model fully explainable. A model trained with RLHF may behave better, but the reasons for a specific output can still be difficult to inspect.

For example, the model may answer politely and follow instructions, but that does not reveal which training examples, reward signals or internal representations influenced the response. This is why RLHF and model explainability are related but different topics.

In practical systems, RLHF should be combined with evaluation, monitoring, logging, source attribution and other methods that make model behaviour easier to inspect.

Alternatives and related approaches

RLHF is not the only way to shape model behaviour. Several related approaches are used or researched.

• Supervised fine-tuning – training the model on examples of desired outputs.
• Direct Preference Optimization – optimising a model directly from preference data without the same classic RLHF pipeline.
• RLAIF – reinforcement learning from AI feedback, where another AI system helps provide feedback.
• Constitutional AI – using written principles or rules to guide model behaviour and feedback.
• Rejection sampling – generating multiple outputs and selecting the best one according to a scoring method.
• Rule-based safety layers – adding external checks or filters around the model output.

These approaches are not always direct replacements for each other. In real systems, several methods may be combined.

Where RLHF is used

RLHF is mainly associated with language models and conversational AI, but the broader idea of learning from human preferences can apply to other areas as well.

• AI assistants – improving helpfulness, tone, refusal behaviour and instruction following.
• Chatbots – making responses more relevant, polite and useful.
• Content generation – shaping style, structure and safety of generated text.
• Summarisation – encouraging summaries that humans judge as more useful and faithful.
• Code assistance – preferring outputs that are correct, readable and aligned with the task.
• Robotics – learning complex behaviours from human preference comparisons.
• Recommendation systems – aligning ranking or selection with human judgement and long-term satisfaction.

Common misunderstandings about RLHF

RLHF is often described too simply. It is not just “humans teach the model what is right”. The real process is more indirect and more limited.

• RLHF does not give the model human values – it trains the model on patterns in collected feedback.
• RLHF does not guarantee truth – preferred answers can still be wrong.
• RLHF is not the same as manual moderation – humans provide training signals, not real-time approval of every output.
• RLHF does not remove the need for evaluation – the model still needs testing and monitoring.
• RLHF does not fully explain model behaviour – it can shape behaviour without making the internal process transparent.
• RLHF is not always better than simpler methods – for some tasks, supervised fine-tuning, retrieval or rule-based controls may be more suitable.

How to remember RLHF

RLHF can be compared to training someone not only by showing examples, but also by giving feedback on which attempts are better. If two answers are possible, humans say which one is more helpful, safer or clearer. The system then learns from many such preferences.

The important point is that RLHF does not directly encode perfect human judgement into the model. It creates a training signal from human evaluations and uses that signal to adjust future behaviour.

Related terms

• Machine learning – the broader field in which systems learn patterns from data, examples, feedback or experience.
• Large language model (LLM) – a language-focused AI model. RLHF is often used to make LLMs more helpful, safer and better at following user instructions.
• Prompt engineering – the practice of designing prompts so that language models produce more useful, structured and controllable outputs.
• Embedding – a numerical representation of content such as text, images or documents. Embeddings and RLHF are different concepts, but both show how AI systems convert complex information into numerical form.
• Multimodal models – AI models that work with more than one type of input, such as text, images, audio, video or documents.
• Reinforcement learning – a learning approach where an agent improves its behaviour using rewards or penalties from an environment.
• Reward model – a model trained to estimate which outputs humans are likely to prefer.
• Human preference data – comparisons, rankings or ratings provided by people and used to guide model training.
• Policy optimisation – the process of adjusting a model’s behaviour so that it receives higher reward scores.
• Alignment – the goal of making model behaviour better match human intentions, instructions and safety expectations.
• Reward hacking – a situation where a model learns to optimise the reward signal while missing the real purpose of the task.
• DPO – direct preference optimization, an approach that uses preference data without the same classic RLHF training pipeline.

Sources and further reading

• Illustrating Reinforcement Learning from Human Feedback – huggingface.co – June 2026 – a clear overview of the RLHF pipeline, including language model pretraining, reward model training and reinforcement learning based optimisation.
• Learning from human preferences – openai.com – June 2026 – describes research on using small amounts of human feedback to train systems in reinforcement learning environments.
• What is Reinforcement Learning From Human Feedback? – ibm.com – June 2026 – explains RLHF as a technique where a reward model is trained with human feedback and then used to optimise AI behaviour.
• Deep reinforcement learning from human preferences – arxiv.org – June 2026 – research paper showing how human preference comparisons can be used to train agents without direct access to a hand-written reward function.
• CS324 – Large Language Models – stanford.edu – June 2026 – course materials that place RLHF in the broader context of large language models, alignment and model behaviour.
• Reinforcement Learning from Human Feedback – rlhfbook.com – June 2026 – a book-length introduction to RLHF and post-training methods for language models.