Simplifying AI Learning: A Step-by-Step Guide to Reinforcement Learning from Human Feedback

Reinforcement Learning from Human Feedback (RLHF) is like an AI tutor, shaping our language models by listening to human input.

In this article, we're going to break down the process of RLHF and look at how we train language models with human feedback.

Let's explore the importance of a pre-trained language model (LM) and how a reward model learns from human feedback to make the language model more clever and more human-friendly.

The RLHF Workflow: AI's Learning Journey

The process of RLHF is like a journey with different stops along the way:

a. Pre-Trained Language Model (LM): A Foundation in Language First off, we have a pre-trained language model (LM), kind of like a language student who's already learned the basics. It's a starting point that already has a handle on language in general.

b. Collecting Human Feedback Data: The Homework Next, we collect some feedback from human labelers, like homework assignments for our language model. The labelers get prompts, and the language model comes up with text based on these prompts. This gives us a chance to see how the language model handles various situations.

c. Training the Reward Model (RM): The Grading System The next stop is to train a reward model (RM), a bit like setting up a grading system. It uses the feedback from human labelers to assess the quality of the texts the language model comes up with. The labelers evaluate the texts based on things like helpfulness, honesty, and harmlessness.

d. Reward Model Optimization: The Progress Report After we have our grading system (the reward model) in place, it becomes a guide for how the language model can improve. It assigns scores to the texts based on how well they align with what we humans value and prefer. This feedback helps the language model refine its behavior and produce better, more suitable responses.

The Role of the Pre-Trained Language Model (LM): The Language Apprentice

Our pre-trained language model is like the apprentice in this learning journey. It provides the base knowledge, the foundations we can build on.

It's already got a lot of language understanding under its belt, and we use this knowledge to fine-tune its behavior to meet human expectations better.

Training the Reward Model Using Human Feedback Data: Creating a Report Card

When we train the reward model, we're essentially creating a report card for the language model.

We gather feedback from human labelers who assess the language model's texts based on alignment criteria.

They might rank the texts or provide specific feedback on how well the texts meet the desired standards.

This is how the reward model learns to assess the language model's work accurately.

Conclusion: A Journey of AI Learning with Human Help

The process of RLHF is a learning journey where we train language models with the help of human feedback.

We use the pre-trained language model as a starting point, and with the feedback of human labelers, we shape the reward model.

This allows us to refine the language model's behavior to better match what we humans value and expect.

In our next chat, we'll delve into the RL fine-tuning step and explore the role of reinforcement learning algorithms in making the language model even better.

So, stick around to learn more about how RLHF powers up language models with human feedback.