The Sequence Knowlege #907: The Brain Transplant: Distilling Transformers Into Other Architectures

Substack · 3 weeks ago · 16

Researchers are transferring knowledge from transformer models into fundamentally different architectures like state-space models and linear RNNs through cross-architecture distillation, a technique that preserves the capability of the original model despite changing its computational substrate. A key distinction is that previous distillation kept teacher and student in the same architectural family, but this approach breaks that assumption by using entirely different machine types. This capability transfer opens economic opportunities by allowing efficient non-transformer architectures to inherit transformer-level performance, potentially reducing computational costs in deployment.