Transfer learning vs fine-tuning: are these the same thing?

The terms get used almost interchangeably, but they're not quite the same idea. A clear distinction, and why it rarely matters practically but sometimes does.

Written by

Pranjul Rathour

GenAI Engineer at SCULT INDIA, based in Kanpur, India. Builds production RAG systems, fine-tunes open LLMs and ships agentic AI products; has mentored 200+ college developers. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks.

pranjulrathour41@gmail.com LinkedIn GitHub X Instagram Bluesky Dev.to

Walking a room through evaluation criteria

'Transfer learning' and 'fine-tuning' get used almost interchangeably in casual conversation, and for most LLM work that's harmless — but the terms aren't quite the same idea, and the distinction occasionally matters.

The distinction

Transfer learning is the broader concept: using knowledge learned on one task to help with a different task. Fine-tuning is one specific technique for doing transfer learning — continuing to train a pretrained model's weights on new, task-specific data.

When the distinction matters

Using a frozen pretrained model purely as a feature extractor (embeddings) and training a small separate classifier on top is transfer learning, but not fine-tuning, since the base model's weights never change.
LoRA and full fine-tuning are both fine-tuning techniques, both forms of transfer learning.
In casual conversation with practitioners, using either term for LLM fine-tuning specifically is understood fine — the distinction mostly matters in academic or more precise technical writing.

— Pranjul Rathour, GenAI Engineer from Kanpur, India. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at any campus: pranjulrathour41@gmail.com.