A Practical Guide to Reinforcement Learning from Human Feedback
Packt Publishing Limited (Verlag)
978-1-83588-050-0 (ISBN)
Key Features
Master the principles underlying Reinforcement Learning from Human Feedback to apply them to your own AI problem.
Traverse a focused journey into applying RLHF to LLMs.
Learn state-of-the-art and emerging techniques on aligning AI models to human preferences.
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionReinforcement Learning from Human Feedback (RLHF) is a cutting-edge approach to aligning AI systems with human values. By combining reinforcement learning with human input, RLHF has become a critical methodology for improving the safety and reliability of large language models (LLMs).
This book begins with the foundations of reinforcement learning, including key algorithms such as proximal policy optimization, and shows how reward models integrate human preferences to fine-tune AI behavior. You’ll gain a practical understanding of how RLHF optimizes model parameters to better match real-world needs.
Beyond theory, you’ll explore strategies for collecting preference data, training reward models, and enhancing LLM fine-tuning workflows. Common challenges such as cost, bias, and scalability are addressed with practical solutions and AI-driven alternatives.
The final chapters cover emerging methods, advanced evaluation, and AI safety. By the end, you’ll be equipped with the knowledge and skills to apply RLHF across domains, building AI systems that are powerful, trustworthy, and aligned with human values.What you will learn
Master the essentials of reinforcement learning for RLHF
Understand how RLHF can be applied across diverse AI problems
Build and apply reward models to guide reinforcement learning agents
Learn effective strategies for collecting human preference data
Fine-tune large language models using reward-driven optimization
Address challenges of RLHF, including bias and data costs
Explore emerging approaches in RLHF, AI evaluation, and safety
Who this book is forThis book is for AI practitioners looking to implement RLHF in their projects and seeking a single, consolidated resource to guide them. It is equally valuable for researchers and students who want to deepen their understanding of RLHF without navigating scattered research papers. Industry leaders and decision-makers will also benefit, gaining the knowledge to evaluate RLHF and make informed choices about its adoption in AI workflows.
Sandeep (Sandip) Kulkarni is a Principal Applied AI Engineer at Microsoft, where he builds LLM- and RL-powered solutions across Azure Data and Microsoft Fabric. His work spans real-time control, simulators, and LLMOps, with deployments from heavy equipment to chemical processing. Previously at Bonsai and Western Digital, he led simulation and control initiatives. He holds a PhD in Control Engineering (University of Utah) and an MS in Dynamical Systems & Control (UC Davis).
Table of Contents
Introduction to Reinforcement Learning
Role of Human Feedback in Reinforcement Learning
Reward Modeling
Policy Training Based on Reward Model
Introduction to Language Models and Fine Tuning
Parameter Efficient Fine Tuning
Reward Modeling for Language Model Tuning
Reinforcement Learning for Tuning Language Models
Challenges of Reinforcement Learning with Human Feedback
Direct Preference Optimization
RLHF and Model Evaluations
Other Applications
| Erscheinungsdatum | 08.10.2025 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Informatik ► Software Entwicklung ► User Interfaces (HCI) |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-83588-050-9 / 1835880509 |
| ISBN-13 | 978-1-83588-050-0 / 9781835880500 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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