Selective Rotary Position Embedding
Sajad Movahedi, Arshia Afzal, Timur Carstensen, Frank Hutter, Antonio Orvieto, Volkan Cevher
Abstract
Position information is essential for language modeling. In softmax transformers, Rotary Position Embeddings (\textit{RoPE}) encode positions through \textit{fixed-angle} rotations, while in linear transformers, order is handled via input-dependent (selective) gating that decays past key-value associations. Selectivity has generally been shown to improve language-related tasks. Inspired by this, we introduce \textit{Selective RoPE}, an \textit{input-dependent} rotary embedding mechanism, that generalizes \textit{RoPE}, and enables rotation in \textit{arbitrary angles} for both linear and softmax transformers. We show that softmax attention already performs a hidden form of these rotations on query-key pairs, uncovering an implicit positional structure. We further show that in state-space models and gated linear transformers, the real part manages forgetting while the imaginary part encodes positions through rotations. We validate our method by equipping gated transformers with \textit{Selective RoPE}, demonstrating that its input-dependent rotations improve performance in language modeling and on difficult sequence tasks like copying, state tracking, and retrieval.
BibTeX
@inproceedings{
movahedi2026selective,
title={Selective Rotary Position Embedding},
author={Sajad Movahedi and Arshia Afzal and Timur Carstensen and Frank Hutter and Antonio Orvieto and Volkan Cevher},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=AQo1SEElNb}
}