The 5 Steps of Reinforcement Learning with Human Feedback

The Key to Unlocking the Full Potential of Large Language Models

How RLHF Works 

Reinforcement learning is revolutionizing the way we approach complex problems in the world of technology and business. It’s a powerful tool that enables machines to learn from their environment and make informed decisions based on rewards and punishments.

But what if we could combine the power of reinforcement learning with the human touch?  

That’s where the concept of reinforcement learning with human feedback comes into play. In this article, we’ll take a closer look at the five key steps involved in this cutting-edge approach and explore how it’s changing the game for tech enthusiasts and business leaders alike. From improving customer experiences to optimizing complex processes, the possibilities of reinforcement learning with human feedback are truly limitless.  

So, let’s dive in and discover what the future holds for this exciting technology. 

The 5 Steps of Reinforcement Learning with Human Feedback 

  1. Starting with a pre-trained model: You begin by using a pre-trained model that’s been trained on a vast amount of data to generate outputs for a specific task. 

  2. Supervised fine-tuning: The pre-trained model is then further trained on a specific task or domain with labeled data, allowing it to generate more accurate and relevant outputs for the specific task. 

  3. Reward model training: A reward model is trained to recognize desirable outputs generated by the generative model and assign a score based on relevance and accuracy to the desired outcome. This helps reinforce the generative model’s learning and improve the quality and relevance of the generated outputs.

  4. Reinforcement learning via proximal policy optimization (PPO): This technique allows the model to learn from experience and adapt to new situations in real-time. It interacts with an environment and receives feedback in the form of rewards or penalties, allowing it to learn which actions lead to desirable outcomes. 

  5. Red teaming: Finally, the system is stress-tested by a curated crowd to ensure it’s able to handle real-world scenarios and make accurate and relevant predictions. 

Step 0: Defining Your Problem Space 

Developing AI applications that are effective, reliable, and ethical requires a well-considered approach from the outset. When it comes to Reinforcement Learning with Human Feedback (RLHF), incorporating diverse perspectives is essential, as it relies on humans to determine what constitutes an acceptable response and train the model accordingly. This means considering the perspectives of individuals of all genders, ages, languages, domain expertise, social and cultural backgrounds, and all walks of life. 

However, simply hiring a group of click-workers isn’t enough. To ensure the AI application is not biased and represents the perspectives of all individuals, a diverse crowd must be thoughtfully curated and trained to use their best judgment when teaching the model and assessing its outcomes. Before deploying the AI application, careful consideration must also be given to its intended purpose, potential impact, and required inputs, with a focus on ensuring that marginalized populations are represented in the development process.

This is where the expertise of partners like Appen come into play. With more than 25 years of experience curating and managing a diverse crowd of AI Training Specialists, providing clear and meaningful instructions, and analyzing data outcomes, Appen is a trusted partner in building generative AI applications responsibly. 

Through careful consideration of all perspectives and potential impacts, we can unlock the full potential of RLHF and create AI applications that are both effective and ethical. 

Step 1: Start with a Pre-trained Model 

The first step in developing AI applications using Reinforcement Learning with Human Feedback involves starting with a pre-trained model, which can be obtained from open-source providers such as Open AI or Microsoft or created from scratch. Starting with a pre-trained model is often the most efficient approach, as it allows you to fine-tune the model for your specific use case by providing proper prompts and responses. 

The process of prompt generation is a critical step and involves developing many unique prompts based on intent and problem areas. By providing an initial prompt dataset, you can guide the model to generate output that is relevant and coherent in the context of your application. This ensures the output generated by the model is accurate and aligned with your goals, and it sets the stage for the subsequent steps in the Reinforcement Learning with Human Feedback process. 

Step 2: Supervised Fine-Tuning 

Supervised fine-tuning is a crucial step in the development of generative AI applications for large language models, allowing them to become more versatile and adaptable to specific use cases. Fine-tuning a pre-trained model involves data to provide specific examples for the model to learn from and adapt to the task at hand. 

During this step, the weights of the pre-trained model are adjusted based on the new data, enabling it to generate more accurate and relevant outputs for the specific task. Without fine-tuning, the pre-trained model may struggle to produce relevant or useful outputs for the given task. By giving your AI training specialists a prompt, they can create the desired response that the model should give and use domain-specific data to fine-tune the model accordingly.

Fine-tuning not only enhances the efficiency and accuracy of a large language model, but also helps reduce bias and ensures the model outputs align with the desired outcomes for the task. This makes the system more effective and useful for real-world applications. With Appen’s expertise in providing domain specific data, fine-tuning models becomes a breeze and you can trust that your generative AI applications will generate high-quality and relevant outputs that meet your specific needs. 

Step 3: Reward Model Training 

Reward model training is an advanced technique used in Reinforcement Learning with Human Feedback that involves training a model to recognize desirable outputs created by another model and assign scores based on relevance and accuracy to the desired outcome. This process involves training the reward model separately from the generative model and using the scores from the reward model as feedback to fine-tune the generative model to produce more desirable outputs. 

By using these scores as feedback, the generative model can be fine-tuned to create outputs that are more likely to receive high scores from the reward model. This approach is particularly useful for complex or difficult-to-define outcomes, allowing the model to learn from examples rather than explicit instructions. Reward model training can also help address bias and ethical concerns by providing a clear objective function to optimize for. 

Appen’s platform is an excellent tool for implementing this technique, as it provides a reliable means of ranking model responses and selecting the one that provides the clearest response and action to the given query. The AI trainer can utilize the platform to provide data to update the reward model and ensure the LLM generates outputs that meet the desired outcomes for the task at hand. By leveraging Appen’s expertise, you can be confident that your generative AI system will deliver high-quality outputs that meet your specific needs. 

Step 4: Reinforcement learning via proximal policy optimization (PPO) 

Reinforcement learning via proximal policy optimization (PPO) is a type of algorithm that trains large language models to produce outputs that maximize a reward signal through trial and error. In this approach, the model interacts with an environment and receives feedback in the form of rewards or penalties, allowing it to learn which actions lead to desirable outcomes. The goal is to learn a policy that maximizes the expected cumulative reward over a sequence of actions, given a particular state, while also constraining the magnitude of updates to prevent large deviations. 

Reinforcement learning via PPO enables models to learn from experience and adapt to new situations in real-time. This makes it suitable for applications where the desired outcome may be difficult to define or change over time, such as game playing, robotics, or natural language processing.  

The PPO algorithm is used to adjust the model’s behavior overtime and prevents large, abrupt changes. This approach makes it stable and more effective. The reward model is a component in a machine learning system that scores the model’s behavior in the real world and incentivizes the model to achieve the highest score possible. With the combination of these two, you get a consistent improvement over time. 

Having a diverse and curated crowd consistently stress-testing the system can enable it to learn and evolve just as humans do. This can help the model produce outputs that are not only accurate and relevant but also aligned with human values, ethics, and fairness. Generative AI systems trained with reward model training and PPO can achieve impressive results and offer significant benefits in a variety of domains, making them a powerful tool for businesses and organizations seeking to innovate and solve complex problems. 

Step 5: Red teaming 

Red Teaming is a crucial part of the RLHF process, as it allows for human evaluators to provide real-world feedback on the performance of the generative AI models. The human evaluators, often referred to as the crowd, are a diverse group of people with varying backgrounds and experiences, which helps to ensure that the models are evaluated from different perspectives. With red teaming, the generative AI models can be tested for accuracy, relevance, and coherence in a variety of scenarios, such as real-world situations, edge cases, and unforeseen circumstances. The insights gained from red teaming can then be used to further refine and improve the models, ensuring that they are well-suited for the intended use case. 

Building generative AI applications that are responsible and unbiased is critical for their successful implementation in real-world settings. Appen’s expertise in curating and managing a diverse crowd, providing meaningful instructions, and analyzing data outcomes makes us a reliable partner in building generative AI applications responsibly. Our capabilities in RLHF enable us to leverage human feedback to teach models to make accurate and relevant decisions, while also addressing issues of bias and ethical concerns. With our focus on ethical AI and commitment to delivering the most accurate and relevant results, Appen is your trusted partner in building generative AI applications that benefit society as a whole. Let us help you unlock the potential of generative AI and make a positive impact in your domain. 

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Andrew Ettinger | Chief Revenue Officer

Andrew Ettinger joined Appen as Chief Revenue Officer in May 2023 overseeing the company's revenue strategies and driving growth in the field of AI. He joined Appen with more than 25 years of sales experience in sales and services in the technology industry. 

Andrew's expertise extends to harnessing the power of data to drive insights and optimize processes. As the Chief Revenue Officer at Astronomer, he successfully grew the adoption of their open-source data solution, leading to a remarkable increase in monthly downloads and revenue. His strategic initiatives resulted in a 600% growth in customer count and a 75% win rate. 

Prior to joining Astronomer, he served as the VP of Sales at Pivotal Software, where he helped grow the business from zero to $100 million in annual recurring revenue in a single year leading up to Pivotal’s initial public offering, and up to $500 million thereafter. Under his leadership, the company achieved three consecutive years of 50% revenue growth, fueling digital transformations for Fortune 500 companies in various sectors. 

Andrew holds a Bachelor of Science in Business Marketing from The Ohio State University.