First Look at Reasoning From Scratch: Chapter 1

First Look at Reasoning From Scratch: Chapter 1

As you know, I've been writing a lot lately about the latest research on reasoning in LLMs. Before my next research-focused blog post, I wanted to offer something special to my paid subscribers as a thank-you for your ongoing support. So, I've started writing a new book on how reasoning works in LLMs, and here I'm sharing the first Chapter 1 with you. This ~15-page chapter is an introduction reasoning in the context of LLMs and provides an overview of methods like inference-time scaling and reinforcement learning. Thanks for your support! I hope you enjoy the chapter, and stay tuned for my next blog post on reasoning research!

The State of LLM Reasoning Models

The State of LLM Reasoning Models

This article explores recent research advancements in reasoning-optimized LLMs, with a particular focus on inference-time compute scaling that have emerged since the release of DeepSeek R1.

Understanding Reasoning LLMs

Understanding Reasoning LLMs

In this article, I will describe the four main approaches to building reasoning models, or how we can enhance LLMs with reasoning capabilities. I hope this provides valuable insights and helps you navigate the rapidly evolving literature and hype surrounding this topic.

Noteworthy LLM Research Papers of 2024

Noteworthy LLM Research Papers of 2024

This article covers 12 influential AI research papers of 2024, ranging from mixture-of-experts models to new LLM scaling laws for precision.

Implementing A Byte Pair Encoding (BPE) Tokenizer From Scratch

Implementing A Byte Pair Encoding (BPE) Tokenizer From Scratch

This is a standalone notebook implementing the popular byte pair encoding (BPE) tokenization algorithm, which is used in models like GPT-2 to GPT-4, Llama 3, etc., from scratch for educational purposes."