Welcome to an exciting journey into the world of open-source AI with reinforcement learning! This chapter is designed to help students understand reinforcement learning and its role in LLMs.
We will also explore Open R1, a groundbreaking community project that’s making advanced AI accessible to everyone. Specifically, this course is to help students and learners to use and contribute to Open R1.
In this chapter, we’ll break down complex concepts into easy-to-understand pieces and show you how you can be part of this exciting project to make LLMs reason on complex problems.
LLMs have shown excellent performance on many generative tasks. However, up until recently they have struggled on complex problems that require reasoning. For example, they struggle to deal with puzzles or math problems that require multiple steps of reasoning.
Open R1 is a project that aims to make LLMs reason on complex problems. It does this by using reinforcement learning to encourage LLMs to ‘think’ and reason.
In simple terms, the model is train to generate thoughts as well as outputs, and to structure these thoughts and outputs so that they can be handled separately by the user.
Let’s take a look at an example. A we gave ourself the task of solving the following problem, we might think like this:
Problem: "I have 3 apples and 2 oranges. How many pieces of fruit do I have in total?"
Thought: "I need to add the number of apples and oranges to get the total number of pieces of fruit."
Answer: "5"
We can then structure this thought and answer so that they can be handled separately by the user. For reasoning tasks, LLMs can be trained to generate thoughts and answers in the following format:
<think>I need to add the number of apples and oranges to get the total number of pieces of fruit.</think> 5
As a user, we can then extract the thought and answer from the model’s output and use them to solve the problem.
As a student, understanding Open R1 and the role of reinforcement learning in LLMs is valuable because:
This chapter is divided into four sections, each focusing on a different aspect of Open R1:
We’ll explore the basics of Reinforcement Learning (RL) and its role in training LLMs.
We’ll break down the research paper that inspired Open R1:
We’ll get practical with code examples:
We’ll look at a practical use case to align a model using Open R1.
To get the most out of this chapter, it’s helpful to have:
Don’t worry if you’re missing some of these – we’ll explain key concepts as we go along! 🚀
If you don’t have all the prerequisites, check out this course from units 1 to 11
Let’s begin our exploration of Open R1 and discover how you can be part of making AI more accessible to everyone! 🚀
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