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arXiv · 2601.02728

CRoPE: Efficient Parametrization of Rotary Positional Embedding

Beicheng Lou·Zifei Xu·Vivian W. H. Wong
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Abstract

Rotary positional embedding has become the state-of-the-art approach to encode position information in transformer-based models. While it is often succinctly expressed in complex linear algebra, we note that the actual implementation of $Q/K/V$-projections is not equivalent to a complex linear transformation. We argue that complex linear transformation is a more natural parametrization and saves near 50\% parameters within the attention block. We show empirically that removing such redundancy has negligible impact on the model performance. Our modification achieves more efficient parameter usage, as well as a cleaner interpretation of the representation space.

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Beicheng Lou, Zifei Xu, Vivian W. H. Wong. 2026-01-06. CRoPE: Efficient Parametrization of Rotary Positional Embedding. https://arxiv.org/abs/2601.02728

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