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New framework enables efficient knowledge transfer from Transformers to Mamba models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new distillation framework called Cross-architecture distillation via Attention Bridge (CAB) to efficiently transfer knowledge from Transformer models to State Space Models (SSMs) like Mamba. This method enables token-level intermediate supervision by aligning Transformer attention-related representations with Mamba's state projections, facilitating cross-architecture knowledge transfer without increasing inference time. Experiments show CAB improves distillation, especially in low-data scenarios, bridging the gap between mature Transformer ecosystems and emerging SSM ecosystems. AI

IMPACT Facilitates the adoption of emerging State Space Models by leveraging existing Transformer knowledge, potentially accelerating research and development in sequence modeling.

RANK_REASON Academic paper detailing a new method for knowledge distillation between different model architectures. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework enables efficient knowledge transfer from Transformers to Mamba models

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Academic paper detailing a new method for knowledge distillation between different model architectures. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Penghao Wang, Yuhao Zhou, Mengxuan Wu, Panpan Zhang, Zhangyang Wang, Kai Wang ·

    Data Efficient Any Transformer-to-Mamba Distillation via Attention Bridge

    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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