RLHF stands for Reinforcement Learning with Human Feedback.

Reinforcement Learning with Human Feedback.


RLHF is a machine learning approach where a “reward model” is trained using human feedback, which is then used to guide an AI agent’s learning process through reinforcement learning, enabling the agent to optimize its performance and make better decisions.

RLHF has been used by OpenAI, DeepMind, Google and Anthropic

What is RLHF?
Reinforcement Learning with Human Feedback.

RLHF vs Traditional Learning

Traditional Learning

  • Manually defined reward function
  • Human involvement is limited to defining the reward function
  • The model learns to optimize the predefined reward function

RLHF (Reinforcement Learning from Human Feedback)

  • The reward function is learned from human feedback
  • Human involvement is ongoing, providing feedback to guide the learning process
  • The model learns to adapt and personalize its behavior based on human feedback

How does RLHF work?

RLHF working
RLHF working

Here are the three core steps to train a model using Reinforcement Learning from Human Feedback (RLHF):

Step 1: Pretraining a Language Model (LM)

  • Use a pre-trained language model (LM) as a starting point
  • Models can range from small (10 million parameters) to large (280 billion parameters)
  • Examples of starting models include GPT-3, Gopher, and custom-trained models
  • Optional: fine-tune the initial model on additional text or conditions, such as “preferable” text or context clues for desired criteria (e.g., “helpful, honest, and harmless”)

Key Requirement: The initial model should respond well to diverse instructions

Step 2: Gathering Data and Training a Reward Model


  • Create a model that takes in a sequence of text and returns a scalar reward that represents human preference


  • Use human annotators to rank generated text outputs from a language model (LM)
  • Instead of assigning scalar scores, use rankings to compare outputs from multiple models and create a regularized dataset

Ranking methods include:

  • Head-to-head matchups: compare the generated text from two LMs conditioned on the same prompt
  • Elo system: generate a ranking of models and outputs relative to each other
  • Normalize rankings into a scalar reward signal for training


  • Directly applying scalar scores can be difficult due to uncalibrated and noisy human preferences
  • Rankings provide a more reliable and regularized way to capture human preferences


  • A reward model that can be integrated with existing RL algorithms to optimize the performance of the LM

Step 3: Fine-tuning the LM with Reinforcement Learning

Previous Challenges

  • Training a language model with reinforcement learning was considered impossible due to engineering and algorithmic limitations


  • Multiple organizations have successfully fine-tuned a copy of the initial language model (LM) using Proximal Policy Optimization (PPO), a policy-gradient RL algorithm
  • Some parameters of the LM are frozen due to the high cost of fine-tuning large models (10B+ parameters)

Key Factors

  • PPO’s maturity and scalability made it a favorable choice for distributed training in RLHF
  • Core RL advancements focused on updating large models with familiar algorithms like PPO


  • This breakthrough has enabled the successful application of RLHF in various settings, including large-scale language models.

These three steps enable the LM to learn from human feedback and adapt to specific tasks and preferences.

How is RLHF used in the field of generative AI?

RLHF is a crucial technique in generative AI, particularly in Large Language Models (LLMs), to ensure truthful, harmless, and helpful content. 

Its applications extend to other generative AI areas, including:

  • AI Image Generation: RLHF helps evaluate and improve the realism, technicality, or mood of generated artwork.
  • Music Generation: RLHF assists in creating music that matches specific moods and soundtracks for activities.
  • Voice Assistants: RLHF guides the voice to sound more friendly, inquisitive, and trustworthy.

RLHF’s impact on generative AI is significant, as it:

  • Aligns AI output with human values and preferences
  • Enables customization of AI behavior to specific tasks and domains
  • Improves the overall quality and usefulness of generated content

Note that the degree of human value involvement in RLHF is up to the creator, and different models may prioritize values differently. This highlights the importance of responsible AI development and consideration of ethical implications.



RLHF is a powerful technique that has revolutionized the field of generative AI. By leveraging human feedback to train and fine-tune AI models, RLHF enables the creation of more accurate, helpful, and harmless content. 

RLHF’s key benefits include:

  • Improved AI performance and accuracy
  • Enhanced alignment with human values and preferences
  • Customization to specific tasks and domains
  • Increased trust and reliability in AI output

As AI continues to evolve and play a larger role in our lives, RLHF will be crucial in ensuring that AI systems are responsible, ethical, and beneficial to society.

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