Attention Editing: A Versatile Framework for Cross-Architecture Attention Conversion
Summary: arXiv:2604.05688v1 Announce Type: cross
Abstract
Key-Value (KV) cache memory and bandwidth increasingly dominate large language model inference costs 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.
Introduction
In the rapidly evolving field of artificial intelligence, the optimization of large language models (LLMs) is critical for enhancing performance and efficiency. Recent innovations in attention mechanisms have shown promise in addressing the inherent limitations of existing architectures. This article introduces a new approach called Attention Editing, which provides a versatile framework for converting already-trained LLMs to utilize new attention architectures without the need for extensive re-pretraining.
The Challenge
As the complexity of language models increases, so too do the demands on memory and processing bandwidth. The traditional Key-Value caching methods become increasingly inefficient, particularly in scenarios requiring long-context understanding and generation. While newer architectures like MLA and hybrid SWA have emerged as solutions, their integration into existing models presents significant challenges due to their structural demands.
Attention Editing Framework
Attention Editing addresses these challenges by providing a structured yet flexible method for transforming LLMs. This framework operates through the following key components:
- Layer-wise Teacher-forced Optimization: This technique ensures that the new attention layers are effectively trained by using intermediate activation supervision, which helps to mitigate the cold-start errors that can occur during training.
- Model-level Distillation: This process focuses on refining the next-token predictions of the model, with an option to include regularization through weak feature matching to enhance performance further.
Implementation and Results
The framework has been instantiated on two distinct target architectures: MLA and GateSWA, a gated hybrid SWA design. We have applied this methodology to two models, Qwen3-8B and Qwen3-30B-A3B. The results indicate that the modified models maintain competitive performance metrics while achieving significant efficiency improvements.
Practical Training Case Study
Experiments were conducted on the Ascend 910B clusters, which serve as a practical training case study on domestic hardware. The results not only validate the effectiveness of the Attention Editing framework but also demonstrate that large-scale attention conversion is both feasible and robust, paving the way for future advancements in the field.
Conclusion
Attention Editing represents a significant step forward in the field of large language model optimization, offering a practical and efficient pathway for integrating advanced attention architectures into existing models. As the demand for more capable and efficient AI systems continues to grow, methodologies like Attention Editing will play a crucial role in shaping the future of natural language processing.