Fine-Tuning vs Distillation vs Transfer Learning: Whats The Difference? 0 like February 20, 2025Share this postAuthor(s): Artem Shelamanov Originally published on Towards AI. What are the main ideas behind fine-tuning, distillation, and transfer learning? A simple explanation with a focus on LLMs.This member-only story is on us. Upgrade to access all of Medium.Fine-tuning vs distillation vs transfer learning, Image by authorWith the launch of Deepseek-R1 and its distilled models, many ML engineers are wondering: whats the difference between distillation and fine-tuning? And why has transfer learning, very popular before the rise of LLMs, seemingly became forgotten?In this article, well look into their differences and determine which approach is best suited for which situations.Note: While this article is focused on LLMs, these concepts apply to other AI models as well.Although this method was used long before the era of LLMs, it gained immense popularity after the arrival of ChatGPT. Its easy to see the reason behind this rise if you know what GPT stands for Generative Pre-trained Transformer. The pre-trained part indicates that the model was trained already, but it can be further trained for specific goals. Thats where fine-tuning comes in.Fine-tuning, image by authorIn simple terms, fine-tuning is a process where we take a pre-trained model (which has already learned general patterns from a huge dataset) and then train it further on a smaller, task-specific dataset. This helps the model perform better on specialized tasks or domains (like medical advice 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 asponsor. Published via Towards AITowards AI - Medium Share this post