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©2026
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xxiii, 413 pages : color illustrations ; 24 cm
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Understanding reasoning models -- Generating text with a pretrained LLM -- Evaluating reasoning models -- Improving reasoning with inference-time scaling -- Inference-time scaling via self-refinement -- Training reasoning models with reinforcement learning -- Improving GRPO for reinforcement learning -- Distilling reasoning models for efficient reasoning -- Appendix A: References and further reading -- Appendix B: Exercise solutions -- Appendix C: Qwen3 LLM source code -- Appendix D: Using larger LLMs -- Appendix E: Batching and throughput-oriented execution -- Appendix F: Common approaches to model evaluation -- Appendix G: Building a chat interface
LLM reasoning models have the power to tackle truly challenging problems that require finding the right path through multiple steps.In this book you'll learn how to build a working reasoning model from the ground up.You will start with an existing pre-trained LLM and then implement reasoning-focused improvements from scratch
"For readers who know Python and have some knowledge of machine learning"--Page 4 of cover