---
title: 'Scaling Laws for Neural Language Models'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2001-08361-scaling-laws-for-neural-language-models.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Jared Kaplan and 9 coauthors (listed on the arXiv abs page), Scaling Laws for Neural Language Models (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: foundations'
tags: arxiv, scaling-laws, language-models, foundations
arxiv_id: 2001.08361
alias: 2001.08361-scaling-laws-for-neural-language-models
type: pointer
format: pointer
sources:
  - id: S1
    title: 'Scaling Laws for Neural Language Models'
    publisher: arXiv
    authors: 'Jared Kaplan and 9 coauthors (listed on the arXiv abs page)'
    url: https://arxiv.org/abs/2001.08361
    version_read: 'v1 (23 Jan 2020)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# Scaling Laws for Neural Language Models

## Summary
[Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) (2020) gave the first widely used quantitative picture of how language-model loss falls as you scale parameters, dataset size, and compute. The paper turned a loose bigger-is-better intuition into a set of power-law trends that labs could use to plan training runs.

What it introduced: empirical scaling laws relating cross-entropy loss to model size, data, and training compute, and an analysis of the tradeoffs those curves imply for compute-efficient training.

What it led to: the later compute-optimal work often called Chinchilla, which is a pointer note in this batch, and the scale-up path behind GPT-3, Llama, and other foundation models. Those later papers are easier to read if you start here.

Where to read it: https://arxiv.org/abs/2001.08361 (v1, 23 Jan 2020).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

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