---
title: Fine-tuning
description: 'An additional training phase that adapts a pretrained model by updating all parameters or a selected subset on new data.'
canonical_url: 'https://darkfactory.dev/glossary/fine-tuning'
markdown_url: 'https://darkfactory.dev/glossary/fine-tuning.md'
collection: glossary
date_published: '2026-08-03T00:00:00-04:00'
date_modified: '2026-08-26T00:00:00-04:00'
---

# Fine-tuning


## Definition

Fine-tuning continues training from pretrained weights on data chosen for a target task, domain, behavior, or output format. Full fine-tuning updates most or all model parameters. Partial methods freeze some layers or train a small set of added parameters. Low-rank adaptation is one parameter-efficient method, not a synonym for fine-tuning as a whole.

The new data can be labeled examples, instructions and responses, preference pairs, domain text, or another task-specific signal. The objective matters as much as the data. Supervised instruction tuning, continued language-model training, and DPO all change weights, but they optimize different targets and should be named separately when that distinction affects evaluation.

OpenAI's 2018 generative-pretraining work and Google's 2018 BERT work are influential examples of pretraining followed by task fine-tuning. Both showed that one pretrained Transformer could be adapted across several language tasks with limited architecture changes. They popularized the modern workflow, but fine-tuning as a term and practice predates those models.

Fine-tuning can improve a target metric while narrowing other capabilities, worsening calibration, or overfitting a small dataset. Evaluation should therefore cover the intended task, retained base capabilities, safety behavior, and production inputs that differ from the tuning set.

## Distinguish it from nearby terms

Fine-tuning changes model parameters. Prompting and retrieval change runtime context. Tools add external actions or information. Distillation trains a student to imitate a teacher, while fine-tuning adapts the selected model to new data or objectives.

## Check your understanding

A fine-tuned support model follows the company's answer format but loses accuracy on uncommon product versions. Which retained-capability tests should have run alongside the target-format evaluation?

## Also called

finetuning, adaptation

## Related terms

- [Low-rank adaptation (LoRA)](https://darkfactory.dev/glossary/low-rank-adaptation)
- [Pretraining](https://darkfactory.dev/glossary/pretraining)
- [Instruction tuning](https://darkfactory.dev/glossary/instruction-tuning)
- [Direct preference optimization (DPO)](https://darkfactory.dev/glossary/direct-preference-optimization)

## Related factory areas

- [Model selection, routing & budgets](https://darkfactory.dev/factory/model-routing-budgets)

## Evidence and further reading

- [NIST AI 100-2: Adversarial Machine Learning](https://csrc.nist.gov/pubs/ai/100/2/e2025/final)
- [Google Machine Learning Glossary](https://developers.google.com/machine-learning/glossary/)
- [Improving Language Understanding by Generative Pre-Training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)
- [BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)