This article explores the development of reasoning models in LLMs with a focus on specialized applications beyond pre-training and fine-tuning. The author outlines the meaning of reasoning models, their advantages and disadvantages, and the methodology behind DeepSeek R1. The article delves into the four main approaches to enhancing LLM reasoning capabilities, highlighting inference-time scaling, pure RL, supervised fine-tuning, and model distillation. DeepSeek’s innovative approaches, such as skipping supervised fine-tuning and using rewards for accuracy and format, show that reasoning capabilities can emerge without explicit training. The article also discusses the performance of distilled models compared to larger reasoning models, providing insights into the effectiveness of pure SFT in model development.
https://magazine.sebastianraschka.com/p/understanding-reasoning-llms