Retrieval-Aware Distillation for Transformer-SSM Hybrids | ScienceToStartup | ScienceToStartup

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References (26)

[1]

Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism

2025Aviv Bick, Eric P. Xing et al.

[2]

M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models

2025Junxiong Wang, Wen-Ding Li et al.

[3]

Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners

Founder's Pitch

"Transform Transformer models into memory-efficient hybrids that maintain retrieval capabilities, using fewer attention heads."

Efficient Transformers•Score: 6•View PDF ↗

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Retrieval-Aware Distillation for Transformer-SSM Hybrids

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References (26)

Founder's Pitch

"Transform Transformer models into memory-efficient hybrids that maintain retrieval capabilities, using fewer attention heads."

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Retrieval-Aware Distillation for Transformer-SSM Hybrids

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References (26)

Founder's Pitch

"Transform Transformer models into memory-efficient hybrids that maintain retrieval capabilities, using fewer attention heads."

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