新框架实现从Transformer到Mamba模型的知识高效迁移

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一个名为“跨架构注意力桥接蒸馏”（CAB）的新蒸馏框架，以有效地将知识从Transformer模型迁移到Mamba等状态空间模型（SSMs）。该方法通过对齐Transformer的注意力相关表示与Mamba的状态投影，实现token级别的中间监督，从而在不增加推理时间的情况下促进跨架构知识迁移。实验表明，CAB提高了蒸馏效果，尤其是在低数据场景下，弥合了成熟的Transformer生态系统与新兴的SSM生态系统之间的差距。 AI

影响通过利用现有的Transformer知识，促进了新兴状态空间模型的采用，可能加速序列建模的研究和开发。

排序理由学术论文，详细介绍了一种在不同模型架构之间进行知识蒸馏的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Penghao Wang, Yuhao Zhou, Mengxuan Wu, Panpan Zhang, Zhangyang Wang, Kai Wang · 2026-09-15 04:00

数据高效的任意 Transformer 到 Mamba 蒸馏方法，通过注意力桥实现

arXiv:2510.19266v3 Announce Type: replace Abstract: State-space models (SSMs) have emerged as promising alternatives to Transformers for sequence modeling. However, training competitive SSMs from scratch remains computationally intensive, and the ecosystem around them is far less…

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报道来源 [1]

数据高效的任意 Transformer 到 Mamba 蒸馏方法，通过注意力桥实现

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