---
title: 'RoFormer: Enhanced Transformer with Rotary Position Embedding'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is CC BY-NC-ND 4.0 (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/2104-09864-roformer.md
source_license: 'CC BY-NC-ND 4.0'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, Yunfeng Liu, RoFormer: Enhanced Transformer with Rotary Position Embedding (CC BY-NC-ND 4.0). 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, rope, position-embeddings, long-context
arxiv_id: 2104.09864
alias: 2104.09864-roformer
type: pointer
format: pointer
sources:
  - id: S1
    title: 'RoFormer: Enhanced Transformer with Rotary Position Embedding'
    publisher: arXiv
    authors: 'Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, Yunfeng Liu'
    url: https://arxiv.org/abs/2104.09864
    version_read: 'v5 (8 Nov 2023)'
    accessed: 2026-11-11
    license: 'CC BY-NC-ND 4.0'
---
# RoFormer: Enhanced Transformer with Rotary Position Embedding

## Summary
[RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) (2021) introduced rotary position embeddings (RoPE). That encoding is now the default positional method in most large language models, and it is the starting point for a lot of later long-context work.

What it introduced: RoPE, which injects position information by rotating query and key vectors so that relative position is built into the attention scores, rather than adding a separate absolute position vector.

What it led to: the positional scheme used in Llama and many later models, and a line of work on extending context by adjusting those rotations. Live notes on long context include [ao-context-long-context-limits](https://iinventedlight.com/library/notes/ao-context-long-context-limits/) and [ao-context-million-token-windows](https://iinventedlight.com/library/notes/ao-context-million-token-windows/).

If you are reading about context extension or position interpolation, this is the positional method those papers assume. Where to read it: https://arxiv.org/abs/2104.09864 (v5, last revised 8 Nov 2023).

The paper is posted under CC BY-NC-ND 4.0. 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: CC BY-NC-ND 4.0; 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).