AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally
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. Using simplified retrieval tasks and length generalization scenarios, we show -- both empirically and theoretically -- that heads with different functional roles require distinct frequency ranges and attention scaling factors to operate effectively. Ignoring this structure leads to suboptimal utilization of embedding dimensions and degraded performance, particularly under long-context settings. To address these limitations, we propose AdaRoPE, which equips each attention head with lear...
| Authors | Shaowen Wang, Yuke Zheng, Tansheng Zhu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 June 2026 |
| Last updated at source | 11 September 2026 |
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