Mastering ChatGPT with Reinforcement Learning

Table of Contents

1. Introduction
2. Overview of Chat GPT
3. Step 1: Fine-tuning the GPT Network
   • 3.1 Labelers and Fine-tuning Parameters
   • 3.2 Supervised Fine-tuned Model (SFT)
4. Step 2: Generating Multiple Responses
   • 4.1 Ranking the Responses
   • 4.2 Ranking via a La Curt Scale
   • 4.3 Training the Rewards Model
5. Step 3: Using Both Models Together
   • 5.1 Generating Responses Using SFT Model
   • 5.2 Evaluating Responses Using Rewards Model
   • 5.3 Fine-tuning the Parameters
6. How GPT Generates Responses
   • 6.1 Input Prompt and Word Generation
   • 6.2 Probability Distribution and Word Selection
   • 6.3 Updating the GPT Model
7. Loss Function and Proximal Policy Optimization (PPO)
   • 7.1 Maximizing Total Reward
   • 7.2 Rewards Ratio and AdVantage Function
   • 7.3 Clipping and Gradient Updates
   • 7.4 Expectation and Multiple Responses
8. Conclusion

Article

Introduction

In this article, we will Delve into the workings of Chat GPT, a powerful language generation model. We will explore the three steps involved in generating responses using Chat GPT. From fine-tuning the GPT network to using reinforcement learning, we will cover each aspect in Detail and provide a comprehensive understanding of how Chat GPT works.

Overview of Chat GPT

Chat GPT is built upon a pre-trained GPT network that is capable of understanding language in general. To make it specifically Adept at answering user Prompts, the network undergoes fine-tuning. This involves using labeled examples of prompts and responses to train the network to generate appropriate and contextually Relevant replies.

Step 1: Fine-tuning the GPT Network

In this step, labelers provide example prompts and corresponding responses to fine-tune the GPT network. Fine-tuning parameters are adjusted to enhance the network's ability to generate accurate and coherent replies. The result is a supervised fine-tuned model (SFT) that serves as the foundation for generating responses.

Step 2: Generating Multiple Responses

To ensure diversity and improve the quality of responses, a single prompt is passed through the SFT model multiple times. This process generates several alternative responses. These responses are then ranked by labelers Based on factors such as factualness, toxicity, and human-like characteristics. The ranking is done using a scale, known as the ranking via a La Curt scale, which assigns a specific number to each response.

Step 3: Using Both Models Together

In this step, the supervised fine-tuned model and the rewards model work together. An unseen input prompt is passed through the SFT model to generate a response. This response is then evaluated using the rewards model to determine its quality and appropriateness for the given prompt. The reward obtained from the evaluation is then used to fine-tune the parameters of the SFT model, incorporating more human-like characteristics and behavior through reinforcement learning.

How GPT Generates Responses

The GPT model generates responses one word at a time, utilizing the input prompt and the words generated before it. The output is a probability distribution table that assigns probabilities to each possible word to be generated next based on the input and previous Context. Instead of choosing the word with the highest probability, the model samples from the distribution, allowing for more human-like variability in the responses.

Loss Function and Proximal Policy Optimization (PPO)

The loss function used to update the GPT model is based on proximal policy optimization (PPO). PPO aims to maximize the total reward by adjusting the model's parameters. The rewards ratio, which compares the rewards obtained using the old and new parameters, is used to calculate the advantage function. Gradient updates are made while ensuring that they are within a specified range, preventing drastic changes in the model's behavior.

Conclusion

Chat GPT combines fine-tuning, multiple response generation, and reinforcement learning to Create a sophisticated language generation model. By incorporating a rewards model and utilizing PPO, Chat GPT achieves high-quality and contextually appropriate responses. The model's ability to generate diverse and human-like replies makes it a valuable tool for various applications.