Reinforcement Learning from Human Feedback: Alignment and post-training of LLMs

This is the authoritative guide for Reinforcement learning from human feedback, alignment, and post-training LLMs. In this 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.

Aligning AI models to human preferences helps them become safer, smarter, easier to use, and tuned to the exact style the creator desires. Reinforc...