Reinforcement Learning From Human Feedback (RLHF)

What is Reinforcement Learning from Human Feedback (RLHF)?

Reinforcement learning from human feedback (RLHF) is a machine learning (ML) technique where a model uses human feedback to improve its performance over time.


At a high level, RLHF is an alternative form of reinforcement learning, training an ML algorithm with rewards and punishments while it interacts with its environment, while incorporating human feedback throughout the process.

Researchers use RLHF to develop models with self-learning capabilities, which can become progressively more accurate and perform tasks that better align with human needs.

Techopedia Explains the RLHF Meaning

Techopedia explains Reinforcement Learning from Human Feedback

In short, RLHF’s definition is where a developer uses reinforcement learning to build a reward model based on human feedback.

This model introduces a system of rewards and punishments that rewards or penalizes an AI agent based on its actions, in an attempt to incentivize it to perform tasks that better meet human needs.

How Does RLHF Work?

Under RLHF, a pre-trained model is first trained on a set of training data. In the context of a language mode, this would be a large data set composed of text data. By using technologies like natural language processing (NLP), the model can begin to process its training data.

To test the model’s capabilities, a group of human evaluators will interact with the model to assess the overall quality of its outputs and performance on given tasks. These evaluators will rank various outputs generated by the model.

Typically an evaluator will be given an opportunity to like/dislike a response or give feedback in a qualitative survey or written comment. This feedback is used to gauge whether the response was helpful or not, and will be used to finetune the model in the future.

Once this feedback is gathered, it is then used to build an AI reward model, which processes the feedback and rewards the model for taking the right action. The model’s parameters are then fine-tuned and adjusted to maximize the chance of rewards while minimizing the likelihood of penalties.

Fine-tuning the model based on feedback from the reward model and human evaluators helps to improve the original model’s overall performance and accuracy.

RLHF for Language Models

One area of AI where RlHF is heavily used is in the realm of large language models (LLMs, with OpenAI, Anthropic, and Google using it in LLMs, such as ChatGPT, Claude 3, and Google Gemini.

In this context, using reinforcement learning from human feedback helps to increase the quality of outputs by teaching the model to produce outputs that better align with human needs.

At the simplest level, this comes to generating natural language output that’s simple, easy to read, and truthful.

How is RLHF Used in the Field of Generative AI?

As mentioned above, providers like OpenAI, Anthropic, and Google use RLHF to improve the quality of their language model responses.

For instance, OpenAI reportedly uses reinforcement learning from human feedback to make its models “safer, more helpful, and more aligned.” More specifically, the organization used this approach to make InstructGPT better at following instructions than GPT-3.

At the same time, using RLHF in generative AI development can help to reduce the chance of harmful outputs being generated. Human evaluators can help identify responses that are biased or toxic.

Applications of RLHF

Currently, there are many different ways that researchers and organizations can implement RLHF.

These include:


Using RLHF to improve the accuracy and performance of LLMs so that they can more accurately respond to user questions and queries.


Teaching robots to perform complex tasks and movements.

Voice Assistants

Can help voice assistants produce more contextually-aware responses to user input.

Image Generation

Can help text-to-image tools better interpret user inputs and compositional styles.

Music Generation

Teach a text-to-audio tool to create music that matches an emotional theme or mood.

RLHF Pros and Cons

As a development approach, RLHF offers a number of pros and cons to researchers and enterprises. These are as follows:


  • Increased model accuracy
  • Greater learning efficiency
  • Enhanced user satisfaction
  • More natural responses
  • Continuous improvement
  • Highly versatile
  • Less harmful output


  • Difficult to gather human feedback
  • Requires specialist
  • Human evaluators can cause harm
  • Prone to error
  • Less effective at optimizing long conversations
  • Lower transparency

Limitations of RLHF

The main limitation that RLHF has is its reliance on human feedback. While human feedback is useful, evaluators can also spread personal biases and prejudices into their evaluations, which can influence the output of an AI model.

At the same time, during the evaluation process, testers can easily make mistakes and incorrectly evaluate the performance of the LLM on a given task. This can lead to less reliable responses being approved.

RLHF Future Trends

As of 2024, RLHF is in its infancy, but as interest in this technique increases then it has the potential to evolve significantly over the next few years.

One of the biggest shifts we can expect to see is vendors developing new techniques to gather feedback from human evaluators, and developing more sophisticated reward models to consistently incentivize high-quality responses.

The Bottom Line

Reinforcement learning from human feedback is an important technique for ensuring that outputs are useful to users. After all there’s no better judge on whether a response was useful or not than a human being.


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Tim Keary
Technology Specialist

Tim Keary is a freelance technology writer and reporter covering AI, cybersecurity, and enterprise technology. Before joining Techopedia full-time in 2023, his work appeared on VentureBeat, Forbes Advisor, and other notable technology platforms, where he covered the latest trends and innovations in technology. He holds a Master’s degree in History from the University of Kent, where he learned of the value of breaking complex topics down into simple concepts. Outside of writing and conducting interviews, Tim produces music and trains in Mixed Martial Arts (MMA).