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Priming: converting pretrained Transformers into hybrid state-space models

Selected attention layers using substitution-based importance, transferred compatible projections and expanded state capacity, then aligned hidden representations and adapted the full hybrid model with next-token training.

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Selected attention layers using substitution-based importance, transferred compatible projections and expanded state capacity, then aligned hidden representations and adapted the full hybrid model with next-token training.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

Per-input realization existence is not a common low-dimensional exact realization across all inputs; practical conversion is approximate and the selection score is a proxy. Although the abstract emphasizes layerwise alignment, method Section3.2.4 says end-to-end final-hidden-state MSE is the default after ablation. Reported savings exclude source pretraining; throughput uses8H200 GPUs, BF16 weights and FP32 recurrent state. The three-way SSM comparison is limited to the stated8B/50%-hybrid/Qwen setup.

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People and systems

Aditya Chattopadhyay; Elvis Nunez; Prannay Kaul; Benjamin Bowman; Evan Becker; Luca Zancato; David Thomas; Wei Xia; Stefano Soatto

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

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English catalogue narrative

Priming: converting pretrained Transformers into hybrid state-space models

Selected attention layers using substitution-based importance, transferred compatible projections and expanded state capacity, then aligned hidden representations and adapted the full hybrid model with next-token training.

Scientific scope

Per-input realization existence is not a common low-dimensional exact realization across all inputs; practical conversion is approximate and the selection score is a proxy. Although the abstract emphasizes layerwise alignment, method Section3.2.4 says end-to-end final-hidden-state MSE is the default after ablation. Reported savings exclude source pretraining; throughput uses8H200 GPUs, BF16 weights and FP32 recurrent state. The three-way SSM comparison is limited to the stated8B/50%-hybrid/Qwen setup.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Aditya Chattopadhyay; Elvis Nunez; Prannay Kaul; Benjamin Bowman; Evan Becker; Luca Zancato; David Thomas; Wei Xia; Stefano Soatto

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Original sources