RLHF: Understanding Reinforcement Learning from Human Feedback

Reinforcement learning from human feedback (RLHF) is a subcategory of artificial intelligence (AI) that relies on human feedback to provide an automated reward system instead of predefined rewards. 

Traditional reinforcement learning is a form of machine learning that uses interactions, observations, and responses from the environment to help the models attain the maximum rewards when they properly meet their goals. RLHF uses human input in machine learning to improve performance and gain maximum rewards when the machines properly meet goals. The main advantage of using RLHF is that artificial intelligence models that learn from actual human feedback can recognize subtleties and subjectivity.

What is RLHF?

Reinforcement learning from human feedback (RLHF) is a machine learning technique that trains models using direct human feedback to optimize self-learning.

RLHF is part of a group of AI learning methods that use a reward model. The reward is the success or progress of the outputs that incentivize the AI model. The main model tries to achieve the maximum amount of rewards from the rewards model, which helps improve the outputs. 

RLHF differs from other machine learning techniques because its rewards system uses human feedback to tweak a pre-trained model, maximizing the reward and improving the outputs. It constantly collects human feedback in a loop and uses it to elevate how the AI program performs over time. RLHF uses human expertise to make the machine learning process faster and more accurate. The human supervisor may also be able to offer additional feedback from the automated rewards system. However, RLHF can be time-consuming and expensive because the programs use a combination of human input and automatic reward signals, so it’s not always pragmatic.

What sets RLHF apart from RL?

The main difference between RLHF and reinforcement learning (RL) is how they obtain feedback. RL is a technique that trains software to make informed decisions by observing how the environment interacts and responds to the feedback. RLHF includes human feedback, so the technique gives users a clearer idea of human goals, wants, and needs.

Another difference is that RL feedback uses the theory of working toward maximum rewards by meeting predetermined objectives. RLHF feedback is more complex and nuanced because it uses the information it collects from actual humans to predict the user’s preferences and goals better.

Fundamental aspects of RLHF

The core idea of RLHF is to develop a system that learns from human responses and feedback, combining human knowledge with machine learning to produce the most accurate, efficient outcome. RLHF techniques include:

• Agent interactions: RLHF requires an AI system or agent that uses RL to perform tasks, learns from human expertise, and relies on rewards or punishments, depending on the model’s actions.  

• Human demonstrations: RLHF uses human feedback to demonstrate what the agents need to do. Then, the agents imitate the demonstrations to achieve the preferable output. 

• Learning rewards: Reward models provide the value function for the actions you want the agent to achieve by marking them as desirable. Then, the models teach the agent to achieve the maximum cumulative reward signals it receives.

How does RLHF work?

RLHF is a useful machine learning method in which AI combines with human feedback to work more accurately. RLHF involves three steps:

1. Choose a pre-trained model as the main model. Main models can handle large amounts of data that require training, particularly with language models. 

2. Create a rewards model. A rewards model is a second model, in addition to the pre-trained one, that is trained using human feedback. Humans rank two or more samples of model-generated outputs on their performance. This feedback creates the foundation for a rewards system for the performance of the main model.

3. Send the main model the outputs from the rewards model. The feedback will receive a quality score the main model uses to measure and improve performance for future tasks.  

Why is RLHF vital for generative AI?

RLHF is a crucial technique for training generative AI because it helps improve the model continuously and consistently. As humans continue to provide feedback over time, generative AI learns to create more accurate outputs consistently more often.

Generative AI is the next stage of AI, creating content that imitates human interactions. You can train a generative AI model to learn how to interact with programming and human languages, conversations, images, music, and nontraditional programming tasks. RLHF includes tools that teach models how to use many feedback signals, including those from human feedback. This information allows generative AI models to learn from human experience, offering outputs for various scenarios.

RLHF also plays a key role in improving generative AI by reducing errors. When generative AI programs don’t fully understand a user’s input, they can misinterpret it or create answers as best they can. RLHF keeps programs and outputs safe by using humans to ensure the model avoids generating errors, including harmful content like violent imagery or discriminatory language.

The use of RLHF in ChatGPT and Bard

RLHF plays an important role in improving the relevance and accuracy of large language models (LLM), particularly in relation to chatbots, such as  Google’s Bard and ChatGPT. LLMs act as catalysts for chatbots by using trained patterns in data to predict answers when a user submits a prompt. Without specific instructions, LLMs can’t understand the intent of the user. Prompt engineering can help LLMs understand the user, but it can’t handle every user exchange with chatbots. Because RLHF uses human feedback, it makes models more accurate and efficient when dealing with human interaction. 

Pros and cons of implementing RLHF

RLHF has many benefits that can help train AI agents to perform complex tasks and align with human context. However, it also has aspects that need improvement and refinement before it can be properly integrated into everyday life. Here are some of RLHF’s benefits and challenges.

Benefits of RLHF

• Accuracy: RLHF uses human feedback to help AI systems better understand and generate more accurate and contextually correct responses.

• Flexibility: RLHF has the flexibility to allow models to perform proficient conversational AI and content generation using feedback from human trainers.

• Diversity: RLHF allows AI models to receive feedback from human trainers with different backgrounds, experiences, and perspectives, so the model learns to generate outputs that represent a variety of viewpoints and address many different user concerns

Challenges of RLHF

• Cost: Gathering human feedback can be more expensive.

• Subjectivity: Because RLHF relies on subjective human feedback, its results can also be subjective. Humans may disagree with outcomes when evaluating them.

• Inaccuracy: RLHF models can sometimes devise ways to fool human experts or work around their feedback.

Learn more about generative AI with Coursera. 

RLHF can improve the reliability and accuracy of AI models. It connects machine learning with human knowledge, such as generative AI tools. If you want to learn more about the future of generative AI, including RLHF, sign up to learn the basics with DeepLearning.AI’s Generative AI for Everyone on Coursera. In the course, you can learn the skills needed to work with generative AI, including using the necessary tools and applying gen AI to large language models (LLMs).