Fine-Tuning vs Distillation vs Transfer Learning: What’s The Difference?

Author(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.

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Fine-tuning vs distillation vs transfer learning, Image by author

With the launch of Deepseek-R1 and its distilled models, many ML engineers are wondering: what’s 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, we’ll 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. It’s 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. That’s where fine-tuning comes in.

Fine-tuning, image by author

In 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.

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Published via Towards AI