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Nathan Lambert

If you’ve been following RLHF over the last couple of years — from “how does this even work?” to “why is every model suddenly using it?” — this book does a great job of cutting through the noise. Nathan mixes the math and engineering with the bigger questions around alignment, and he does it in a way that doesn’t feel hand-wavy or mystical. It’s practical, grounded, and surprisingly candid about what actually happens inside modern training pipelines.

Here’s the kind of ground the book covers: how human preference data is collected (and how messy that can get), how policy-gradient methods in RLHF really work, where approaches like DPO and direct alignment fit in, and how RLHF evolved toward things like verifiable rewards. Nathan also shares a bunch of behind-the-scenes stories from building open models like Llama-Instruct, Zephyr, Olmo, and Tülu — the kind of details you don’t usually get unless you’re in the room when the training scripts are being rewritten at 2 a.m.

The book also takes time with the things people often gloss over: evaluation, alignment trade-offs, instruction tuning recipes, and all the practical tricks used in industry to make models feel more human, less brittle, and more predictable. It’s the first time I’ve seen all of this explained cleanly in one place.

If you’re working with LLMs — or planning to — and want a deeper understanding of what actually happens after base model pretraining, this one is worth a look.

• Full details: https://www.manning.com/books/the-rlhf-book

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