Reinforcement Learning with Large Language Models
Published by Pearson
Practical exploration of reinforcement learning by honing large language models for impactful solutions
- An immersive deep dive into advanced concepts of reinforcement learning in the context of LLMs.
- A practical, hands-on approach to fine-tuning LLMs, with a focus on real-world applications such as generating neutral summaries using T5.
- A unique opportunity to understand and apply innovative concepts like RLHF, RLAIF, and Constitutional AI in reinforcement learning.
This training offers an intensive exploration into the frontier of reinforcement learning techniques with large language models (LLMs). We will explore advanced topics such as Reinforcement Learning with Human Feedback (RLHF), Reinforcement Learning from AI Feedback (RLAIF), and Constitutional AI, and demonstrate practical applications such as fine-tuning open source LLMs like FLAN-T5 and GPT-2. This course is critical for those keen on deepening their understanding of reinforcement learning, its latest trends, and its application to LLMs.
What you’ll learn and how you can apply it
- The principles and applications of RLHF and RLAIF in large language models
- How to design and build reward systems at scale
- The process and benefits of fine-tuning large language models like T5
And you’ll be able to:
- Implement reinforcement learning techniques to enhance the performance and utility of large language models.
- Understand and modify the behavior of large language models using reinforcement learning to better align them with desired goals.
- Design and build a reward modeling system to better guide an LLM on how to perform a task effectively.
This live event is for you because...
- You are a data scientist, AI engineer, or machine learning practitioner interested in the latest advancements in reinforcement learning.
- You want to apply reinforcement learning to large language models.
- You seek to improve your practical skills in fine-tuning large language models for specific tasks.
Prerequisites
- Proficiency in Python programming
- A solid understanding of basic machine learning concepts
- Familiarity with the fundamentals of reinforcement learning and natural language processing
Course Set-up
- Attendees will need access to Python and an environment to run Jupyter notebooks (Anaconda distribution recommended).
- Access to the Internet for downloading and accessing course materials.
- A GitHub repository containing all the necessary code and resources will be provided.
Recommended Preparation
- Attend: LLMs, GPT and Prompt Engineering for Developers by Sinan Ozdemir
- Attend: Using Open- and Closed-Source LLMs in Real-World Applications by Sinan Ozdemir
- Attend: LLMs from Prototypes to Production while Optimizing for Real-World Applications by Sinan Ozdemir
- Watch: Introduction to Transformer Models for NLP by Sinan Ozdemir
Recommended Follow-up:
- Read: Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs by Sinan Ozdemir
- Watch: Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs) by Sinan Ozdemir
- Watch: Lesson 4: Deep Reinforcement Learning in Machine Vision, GANs, and Deep Reinforcement Learning by Jon Krohn
- Explore: Getting Started with Data, LLMs and ChatGPT by Sinan Ozdemir
- Audio: AI Unveiled (Audio) by Sinan Ozdemir
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1: Introduction to Reinforcement Learning and Large Language Models (35 minutes)
- Getting started with reinforcement learning and large language models
- Importance and application of reinforcement learning in aligning LLM behavior
- Q&A (5 minutes)
Segment 2: Implementing Reinforcement Learning Techniques with LLMs (55 minutes)
- Steps in enhancing the performance of LLMs using reinforcement learning techniques
- Case study: Re-aligning FLAN-T5 to output more neutral summaries
- Q&A (10 minutes)
- Break (10 minutes)
Segment 3: Building a Reward Modeling System for LLMs (45 minutes)
- Introduction to reward modeling in LLM alignment
- Case study: Design and build a reward modeling system for effective feedback systems
- Q&A (10 minutes)
- Break (5 minutes)
Segment 4: Modifying LLM Behavior Using RLAIF (45 minutes)
- Implementing an RLAIF system using a reward model
- Case Study: Align an LLM to follow instructions using RLAIF
- Q&A (10 minutes)
Course wrap-up and next steps (10 minutes)
Your Instructor
Sinan Ozdemir
Sinan Ozdemir is the founder of Crucible, an AI factory platform that helps teams convert existing workflows into custom models. He is a Y Combinator alum, AI & LLM Advisor at Tola Capital, and the author of multiple books on data science and machine learning including Building Agentic AI, Quick Start Guide to LLMs, and Principles of Data Science. Sinan is a former lecturer of data science at Johns Hopkins University and the founder of Kylie.ai, an enterprise-grade conversational AI platform (acquired 2014). He holds a master's degree in pure mathematics from Johns Hopkins University and is based in San Francisco, California.