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About this paper
Omar Ramadan, Sam Siavoshian, Amir Kashif Saeed, Benjamin A. Johnson, Amin M. E.-A. Diab, Benjamin M. Rodriguez
ZeroGraph trains independent blocks of pretrained language models using clean intermediate states cached from a frozen teacher. Each block maps one stored boundary state to the next, replacing cross-depth optimization dependencies with persistent data. In tested conversion and distillation settings, clean-boundary training passed locked behavior gates, while unanchored local denoising failed after assembly; cache construction prevented wall-time break-even through 16 reuses.
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