Sebastian Raschka shares the first chapter of his new book focused on reasoning in large language models (LLMs). This chapter defines reasoning within LLMs, differentiates it from pattern matching, and explains key methods to enhance LLM reasoning abilities. It also covers basic training stages of LLMs and introduces reasoning methodologies like inference-time scaling and reinforcement learning.
Table of contents
1.1 What Does “Reasoning” Mean for Large Language Models?1.2 A Quick Refresher on LLM Training1.3 Pattern Matching: How LLMs Learn from DataSort: