Higher-Dimensional Rotary Position Embedding
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The paper investigates the use of partial Rotary Positional Embedding (RoPE) in transformer models, showing that applying RoPE to only about 10% of hidden dimensions can save up to ten times the memory of full RoPE while achieving comparable final loss. The authors systematically analyze training dynamics and convergence across various architectures, model sizes, sequence lengths, and datasets, finding consistent performance trends. They also note that completely omitting positional encoding (NoPE) can cause loss spikes, which are mitigated by minimal RoPE usage or QK‑Norm application.
arXiv:2606. 12146v1 Announce Type: cross Abstract: Rotary Position Embedding (RoPE) is widely adopted in Transformer models, yet its extension to high-dimensional domains lacks a unified theoretical formulation.
arXiv:2607. 19363v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE) is widely adopted in Transformers to encode positional information, yet standard implementations enforce a uniform frequency schedule and scaling across all attention heads.
arXiv:2511. 17388v3 Announce Type: replace-cross Abstract: Position information is essential for language modeling.
arXiv:2609.06712v2 Announce Type: replace Abstract: Diffusion Transformers (DiTs) achieve strong video generation quality, but their dense spatiotemporal self-attention scales quadratically with sequ...
arXiv:2610.06963v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE) encodes token positions by rotating each two-dimensional channel of the query and key vectors at a channel-specific fr...