Selective Rotary Position Embedding

• URL: http://arxiv.org/abs/2511.17388v1
• Date: Fri, 21 Nov 2025 16:50:00 GMT
• Title: Selective Rotary Position Embedding
• Authors: Sajad Movahedi, Timur Carstensen, Arshia Afzal, Frank Hutter, Antonio Orvieto, Volkan Cevher,
• Abstract summary: We introduce textitSelective RoPE, an textitinput-dependent rotary embedding mechanism.<n>We show that softmax attention already performs a hidden form of these rotations on query-key pairs.<n>We validate our method by equipping gated transformers with textitSelective RoPE, demonstrating that its input-dependent rotations improve performance in language modeling.
• Score: 84.22998043041198
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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