Academic

Attention Editing: A Versatile Framework for Cross-Architecture Attention Conversion

arXiv:2604.05688v1 Announce Type: new Abstract: Key-Value (KV) cache memory and bandwidth increasingly dominate large language model inference cost in long-context and long-generation regimes. Architectures such as multi-head latent attention (MLA) and hybrid sliding-window attention (SWA) can alleviate this bound, but integrating them into existing models remains difficult. Prior methods impose fine-grained structural requirements on both source and target attention modules, which cannot meet the feasible requirement in practical deployment. We present Attention Editing, a practical framework for converting already-trained large language models (LLMs) with new attention architectures without re-pretraining from scratch. Attention editing replaces the original attention with a learnable target module and trains it using progressive distillation, consisting of (1) layer-wise teacher-forced optimization with intermediate activation supervision to prevent cold-start error accumulation, a

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Zhen Cheng, Hao-Bo Yang, Wan-Yi Huang, Jin-Long Li · · 1 min read · 4 views
#cs.CL #cs.AI

arXiv:2604.05688v1 Announce Type: new Abstract: Key-Value (KV) cache memory and bandwidth increasingly dominate large language model inference cost in long-context and long-generation regimes. Architectures such as multi-head latent attention (MLA) and hybrid sliding-window attention (SWA) can alleviate this bound, but integrating them into existing models remains difficult. Prior methods impose fine-grained structural requirements on both source and target attention modules, which cannot meet the feasible requirement in practical deployment. We present Attention Editing, a practical framework for converting already-trained large language models (LLMs) with new attention architectures without re-pretraining from scratch. Attention editing replaces the original attention with a learnable target module and trains it using progressive distillation, consisting of (1) layer-wise teacher-forced optimization with intermediate activation supervision to prevent cold-start error accumulation, and (2) model-level distillation on next-token distributions, optionally regularized by weak feature matching. We instantiate the framework on two different target--MLA and GateSWA, a gated hybrid SWA design, and apply it to Qwen3-8B and Qwen3-30B-A3B. The resulting models maintain competitive performance while delivering substantial efficiency improvements, demonstrating that large-scale attention conversion is both feasible and robust. Notably, experiments are conducted on an Ascend 910B clusters, offering a practical training case study on domestic hardware.

Executive Summary

The article introduces *Attention Editing*, a novel framework designed to convert pre-trained large language models (LLMs) to new attention architectures (e.g., Multi-head Latent Attention (MLA) and GateSWA) without requiring full retraining. By leveraging progressive distillation—layer-wise teacher-forced optimization and model-level distillation—the framework mitigates cold-start errors and preserves performance while improving efficiency. The approach is validated on Qwen3-8B and Qwen3-30B-A3B models, demonstrating competitive performance alongside significant computational savings. The study also provides a practical case study on domestic hardware (Ascend 910B), highlighting the framework’s scalability and feasibility for real-world deployment.

Key Points

  • ▸ Introduces *Attention Editing*, a framework for converting pre-trained LLMs to new attention architectures without retraining from scratch.
  • ▸ Uses progressive distillation with layer-wise teacher-forced optimization and model-level distillation to ensure stable convergence and performance retention.
  • ▸ Demonstrates practical viability by converting Qwen3-8B and Qwen3-30B-A3B models to MLA and GateSWA architectures, achieving efficiency gains without sacrificing performance.
  • ▸ Case study on Ascend 910B hardware underscores the framework’s scalability and real-world applicability, particularly for domestic computing infrastructure.

Merits

Innovative Framework Design

The *Attention Editing* framework addresses a critical gap in LLM deployment by enabling attention architecture conversion without retraining, which is computationally prohibitive for large models. The use of progressive distillation ensures stability and performance retention.

Comprehensive Validation

The framework is validated on two distinct target architectures (MLA and GateSWA) and two model sizes (Qwen3-8B and Qwen3-30B-A3B), demonstrating versatility and scalability. The hardware case study on Ascend 910B further strengthens its practical relevance.

Efficiency and Performance Trade-offs

The article successfully balances efficiency gains (e.g., reduced KV cache memory and bandwidth) with competitive performance, addressing a key bottleneck in long-context and long-generation regimes for LLMs.

Demerits

Hardware Dependency

While the use of Ascend 910B hardware demonstrates practicality, the framework’s reliance on specific hardware may limit its immediate applicability to other platforms, potentially restricting broader adoption until further validation is conducted.

Generalizability Concerns

The framework is demonstrated on Qwen3 models and two specific attention architectures (MLA and GateSWA). Its performance on other architectures or model families (e.g., decoder-only models like Llama or encoder-decoder models like T5) remains untested, raising questions about generalizability.

Distillation Complexity

The progressive distillation process involves multiple stages (layer-wise and model-level), which may introduce complexity in implementation and training. The computational overhead of these steps, while justified by the results, could pose challenges for resource-constrained environments.

Expert Commentary

The *Attention Editing* framework represents a significant advancement in the practical deployment of large language models, particularly in addressing the intractable challenge of KV cache memory and bandwidth bottlenecks during inference. By introducing a learnable target module and progressive distillation, the authors have developed a robust method for converting pre-trained models to new attention architectures without sacrificing performance. This is a notable contribution to the field, as it bridges the gap between theoretical architectural innovations and real-world deployment constraints. The use of teacher-forced optimization and intermediate activation supervision is particularly clever, as it mitigates the cold-start problem that often plagues distillation-based approaches. However, the framework’s reliance on specific hardware (Ascend 910B) and its untested generalizability to other model architectures may limit its immediate impact. Future work should explore the framework’s applicability to a broader range of models and hardware platforms, as well as its integration with other optimization techniques such as quantization or pruning. Overall, this work sets a new benchmark for attention architecture conversion and underscores the importance of practical, hardware-aware solutions in AI deployment.

Recommendations

  • ✓ Conduct further validation of the *Attention Editing* framework on a broader range of model architectures (e.g., Llama, T5, Mistral) and hardware platforms (e.g., NVIDIA GPUs, AMD Instinct) to assess its generalizability and scalability.
  • ✓ Explore the integration of *Attention Editing* with other model optimization techniques, such as quantization, pruning, or low-rank adaptation, to further enhance efficiency and performance in deployment scenarios.
  • ✓ Develop open-source toolkits or libraries to facilitate the adoption of *Attention Editing* by the broader AI community, including support for diverse hardware platforms and attention architectures.
  • ✓ Investigate the ethical and governance implications of frameworks like *Attention Editing*, particularly in terms of auditing distillation processes, ensuring fairness, and compliance with emerging AI regulations.
  • ✓ Collaborate with hardware manufacturers to optimize the framework for a wider range of computing platforms, reducing dependency on specific hardware and broadening its applicability.

Sources

Original: arXiv - cs.CL

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