A field guide to AI positions

Model distillation

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Publication dates and source age

Sources counted: 1

Newest dated source: 2015-03-09

Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

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In plain language

Training a model to match outputs from another model, often to make it cheaper to run. [1]

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Limits & distinctions

The smaller model may not match every output or capability. Distillation does not guarantee identical performance. [1]

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A fuller explanation

In the cited method, a larger model supplies probabilities as training targets for a smaller one. The target is output behavior, rather than copying the original model’s weights. [1]

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How it relates to the map

Ask which tasks the smaller model was tested on and how its results changed. [1]

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https://theaiatlas.org/ideas/distillation/

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Sources and what we read

  1. 1. Distilling the Knowledge in a Neural Network

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2015-03-09

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read the introduction and distillation method, including probability targets and imperfect matching. Date checked on arXiv. The historical experiments do not establish performance for current distilled models.

Edition and machine-readable evidence

Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.

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