Title: Rope to Nope and Back Again: A New Hybrid Attention Strategy
Venue: NeurIPS 2025
Accepted: yes
Keywords: LLM

Pretraining

Long Context

Hybrid Attention
Abstract: Long-context large language models (LLMs) have achieved remarkable advancements, driven by techniques like Rotary Position Embedding (RoPE) (Su et al., 2023) and its extensions (Chen et al., 2023; Liu et al., 2024c; Peng et al., 2023). By adjusting RoPE parameters and incorporating training data with extended contexts, we can train performant models with considerably longer input sequences. However, existing RoPE-based methods exhibit performance limitations when applied to extended context lengths. This paper presents a comprehensive analysis of various attention mechanisms, including RoPE, No Positional Embedding (NoPE), and Query-Key Normalization (QK-Norm), identifying their strengths and shortcomings in long-context modeling. Our investigation identifies distinctive attention patterns in these methods and highlights their impact on long-context performance, providing valuable insights for architectural design.  on long context performance, providing valuable insights for architectural design. Building on these findings, we propose a novel architecture featuring a hybrid attention mechanism that integrates global and local attention spans. This design not only surpasses conventional RoPE-based transformer models with full attention in both long and short context tasks but also delivers substantial efficiency gains during training and inference.


Peer reviews:
comments: SUMMARY:
This paper presents a new hybrid attention strategy combining Rotary Position Embedding (RoPE) and No Positional Embedding (NoPE) for long-context large language models (LLMs). The authors analyze existing attention mechanisms and highlight their limitations, then propose an architecture that alternates between RoPE and NoPE layers. This hybrid model outperforms traditional RoPE-based methods on long-context tasks and is efficient in both training speed and memory usage, providing a more effective solution for handling long input sequences.

STRENGTHS AND WEAKNESSES:
## Strengths
1. The paper introduces a unique hybrid attention mechanism, combining RoPE and NoPE, offering an innovative solution for long-context modeling.
2. It provides a clear comparison of existing attention mechanisms, making the analysis accessible and well-structured.
3. The proposed model significantly improves performance and efficiency, making it a valuable contribution to the development of large language models.

## Weaknesses
1. The paper adjusts the model architecture but lacks an analysis of the training overhead introduced by these adjustments.
2. The findings in this paper are similar to those in [1], but the paper does not explain the differences.
3. The paper uses only RoPE as the baseline, lacking validation of the method's effectiveness; it could benefit from a comparison with p-RoPE [1].

[1] Round and Round We Go! What makes Rotary Positional Encodings useful?

QUESTIONS:
See Weaknesses.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-06-23

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper focuses on position embedding for better long-context modeling. Experiments are conducted to compare three position embedding methods, including RoPE, Query-Key normalization, and NoPE. Based on the empirical results, the paper proposes combining rotary position embedding (RoPE) and no position embedding (NoPE) for better long-context performance.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**:\n* The proposed hybrid position embedding method seems effective in improving model performance.\n\n* Generally, the paper is clear and easy to follow.\n\n**Weakness**:\n* It lacks a deep analysis or sufficient supportive empirical evidence for the choice of hybrid position embedding with both RoPE and NoPE. While the paper provides observations in Section 2, there is no analysis on why the RoPE and NoPE are complementary. After all, as shown in Section 2.1 (Figures 1 and 2, Table 2), RoPE and NoPE are similar when the model employs a single position embedding.\n\n* It requires more comparison to the baseline models to truly verify the effectiveness of the proposed method. While a larger $\\theta$ enhances the long-context performance for RoPE, it would only be fair to compare the proposed hybrid position embedding to  RoPE with varying $\\theta$. Meanwhile, the proposed method uses RoPE with sliding window attention; the baseline should also include models with sliding window attention.\n\nQUESTIONS:\n* As mentioned in the Weakness part, more comparisons between the proposed method and various baseline variants (various $\\theta$ values for RoPE,  RoPE with sliding window attention) could further validate the effectiveness of the proposed method. I look forward to further results with more fair comparisons.\n\n* It would be better if the authors could provide a more comprehensive analysis of why choosing NoPE and RoPE for the hybrid position embedding. A deeper analysis of the motivation and insights could strengthen the paper, as it currently only considers three position embedding variants in Section 2.\n\nI look forward to the authors' reply and will adjust my rating accordingly.\n\nLIMITATIONS:\nThe authors could address more of the limitations of the proposed method. Currently, there seems to be little discussion of the limitation.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposed to mix NoPE and RoPE layer by layer interleaved so that it can get the advantages from both methods. NoPE for better retrieval ability, while RoPE for better local ability. They made some observations on the attention distribution of the models, and then use the proposed method to show improvements on the downstream tasks of long context retrieval. Performance gains is more obvious when the context length goes up.\n\nSTRENGTHS AND WEAKNESSES:\nWeaknesses:\n- Why didn't the experiment show NoPE as a baseline? I guess NoPE will still outperform NRoPE on some retrieval tasks, and it will also be good at short context tasks.\n- Insufficient ablations: a good ablation of the method will include the following ablated variants:\n    1. RoPE\n    2. RoPE with swa\n    3. NoPE\n    4. NoPE with swa.  \nFor both accuracy performance and efficiency performance angles to discuss. The current experiments are only with a single “Baseline” which is not professional and not able to prove the effectiveness/efficiency of RNoPE-swa. If efficiency is important, you should change section 5.3 into a table to compare the efficiency of all different variants.\n- The evenly stacking of RoPE and NoPE one by one is not well supported by ablations either. In my opinion, the first few of transformer early layers should be encoding more local informations, so it’s more useful to put RoPE-swa, while the later layers can encode more of the global information that requires NoPE.\n- Again, the ablation of how to mix RoPE/NoPE layers is only in a sentence of “we perform an ablation study on the interleaving ratio of full attention and sliding window layers, testing the configurations of 1:1, 1:3, and 1:7.” Where are your ablation results? How can I trust this sentence without the numbers/significance differences to back it up? There are no tables/numbers for it. The whole paper so far makes me feel like a very non-professional work from an undergrad course project.\n\nQUESTIONS:\n- What does qc mean in the figure 2? If it’s “query and completion”, please explicitly say it.\n- Why is NoPE not included as a baseline?\n- Can you add NoPE and NoPE+SWA to the ablation?\n- Can you include more ablation on the design choices?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a comprehensive analysis of different attention and positional encoding mechanisms in large language models, specifically for handling long contexts. The authors begin by empirically evaluating the performance and attention patterns of models using Rotary Position Embedding (RoPE), No Positional Embedding (NoPE), and Query-Key Normalization (QK-Norm). They identify that NoPE excels at information retrieval over long distances by relying on semantic similarity, but lacks in general performance. Conversely, standard RoPE models with extensions struggle with very long contexts, and QK-Norm, while stabilizing training, degrades long-context performance by creating noisy, unfocused attention patterns.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n- The paper is exceptionally well-written and structured. The narrative flows logically from a clear problem statement, through a comprehensive analysis of existing methods, to the proposal and validation of a new solution. The use of attention pattern visualizations (Fig. 1, 2) provides strong, intuitive evidence for the paper's claims about the behavior of RoPE, NoPE, and QK-Norm, making the motivation for the hybrid model very convincing.\n- The contribution is highly timely. Improving long-context performance while simultaneously increasing computational efficiency is an important goal in LLM research. The proposed RNope-SWA architecture offers a practical and effective solution that addresses both performance and efficiency, a rare combination. The results, particularly the near-perfect scores on NIAH at 256k and the substantially smaller performance drop on Ruler (Table 6), are impressive and demonstrate a clear state-of-the-art advancement over the baseline.\n- While the components themselves (RoPE, NoPE, SWA) are not new, their combination into a functionally specialized, interleaved architecture is novel and insightful. The core idea that different layers can be specialized for different tasks (long-range retrieval vs. local modeling) is powerful. The paper provides strong empirical backing for this \"division of labor\" hypothesis, moving beyond simple engineering to offer a new architectural principle.\n\nWeakness\n\n- The authors state that they performed an ablation study on the interleaving ratio of NoPE to RoPE+SWA layers, testing 1:1, 1:3, and 1:7, and found 1:3 to be optimal (lines 217-219). However, the full results of this important ablation is not presented in the paper or appendix. This is a key design parameter, and its justification currently relies on a statement rather than evidence.\n- The paper makes a key observation that a large RoPE theta value in the hybrid model \"introduces noise into the overall architecture, which disrupts the NoPE layers' ability to...perform retrieval effectively\" (lines 184-186). While the data supports this observation, the paper could benefit from a deeper explanation or hypothesis as to why this occurs. Is it due to the flatter attention distributions from RoPE interfering with the sharp, similarity-based attention of NoPE? A more detailed discussion would strengthen the theoretical underpinnings.\n\nQUESTIONS:\nSee weakness\n\nLIMITATIONS:\nNo limitations", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}