---
title: 'REALM: Retrieval-Augmented Language Model Pre-Training'
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/2002-08909-realm.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Ming-Wei Chang, REALM: Retrieval-Augmented Language Model Pre-Training (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: RAG'
tags: arxiv, rag, retrieval, pretraining
arxiv_id: 2002.08909
alias: 2002.08909-realm
type: pointer
format: pointer
sources:
  - id: S1
    title: 'REALM: Retrieval-Augmented Language Model Pre-Training'
    publisher: arXiv
    authors: 'Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Ming-Wei Chang'
    url: https://arxiv.org/abs/2002.08909
    version_read: 'v1 (10 Feb 2020)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# REALM: Retrieval-Augmented Language Model Pre-Training

## Summary
[REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) (2020) jointly pretrains a retriever and a language model, so that retrieval is learned as part of pretraining rather than bolted on later. It is an early learned retrieval-augmented model, and a close neighbor of the original RAG paper.

What it introduced: an end-to-end pretraining setup in which the model learns to retrieve documents that help it fill in masked text, with an index that is updated during training.

What it led to: later retrieval-augmented generators, including RAG (a pointer in batch B), Self-RAG, and today's assistant RAG stacks. Live notes: [genai-rag-embeddings-vector-search](https://iinventedlight.com/library/notes/genai-rag-embeddings-vector-search/) and [2310-11511-self-rag](https://iinventedlight.com/library/notes/2310-11511-self-rag/).

Read it alongside the original RAG paper when you want the pretraining-time version of learned retrieval. Where to read it: https://arxiv.org/abs/2002.08909 (v1, 10 Feb 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.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).