The RLHF Book ( MEAP v2) by Nathan Lambert (.ePUB)+
File Size: 12.4 MB
The RLHF Book: Reinforcement learning from human feedback, alignment, and post-training LLMs ( MEAP v2) by Nathan Lambert
Requirements: .ePUB, .PDF reader, 12.4 MB
Overview: The authoritative guide for Reinforcement learning from human feedback, alignment, and post-training LLMs. Aligning AI models to human preferences helps them become safer, smarter, easier to use, and tuned to the exact style the creator desires. Reinforcement Learning From Human Feedback (RHLF) is the process for using human responses to a model’s output to shape its alignment, and therefore its behavior. In The RLHF Book, author Nathan Lambert blends diverse perspectives from fields like philosophy and economics with the core mathematics and computer science of RLHF to provide a practical guide you can use to apply RLHF to your models. The RLHF Book explores the ideas, established techniques and best practices of RLHF you can use to understand what it takes to align your AI models. You’ll begin with an in-depth overview of RLHF and the subject’s leading papers, before diving into the details of RLHF training. Next, you’ll discover optimization tools such as reward models, regularization, instruction tuning, direct alignment algorithms, and more. Finally, you’ll dive into advanced techniques such as constitutional AI, synthetic data, and evaluating models, along with the open questions the field is still working to answer. All together, you’ll be at the front of the line as cutting edge AI training transitions from the top AI companies and into the hands of everyone interested in AI for their business or personal use-cases. This book is both a transition point for established engineers and AI scientists looking to get started in AI training and a platform for students trying to get a foothold in a rapidly moving industry.
Genre: Non-Fiction > Tech & Devices
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