No Libraries No Shortcuts: Reasoning LLMs from Scratch with PyTorch — Part 2

Author(s): Ashish Abraham Originally published on Towards AI. The no BS Guide to implementing reasoning models from scratch with SFT & RL In Part 1 of this series, we laid the groundwork for understanding how reasoning large language models (LLMs) can be built from first principles using PyTorch. We explored core transformer architecture enhancements like Multi-Query Attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and implemented them from scratch. Image By AuthorThis article delves into the techniques and processes of fine-tuning large language models using both supervised fine-tuning (SFT) and reinforcement learning (RL), focusing on methods like Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO). It elucidates how to implement these techniques from scratch using PyTorch, with an emphasis on the importance of aligning LLMs to enhance their reasoning capabilities while conserving their pre-trained strengths, ultimately providing a comprehensive understanding for developers and researchers interested in advanced LLM constructs. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI