RMT-KD: Random Matrix Theoretic Causal Knowledge Distillation

Davide Ettori; Nastaran Darabi; Sureshkumar Senthilkumar; Amit Ranjan Trivedi

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arXiv · 2509.15724

RMT-KD: Random Matrix Theoretic Causal Knowledge Distillation

Davide Ettori·Nastaran Darabi·Sureshkumar Senthilkumar·Amit Ranjan Trivedi

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Abstract

Large deep learning models such as BERT and ResNet achieve state-of-the-art performance but are costly to deploy at the edge due to their size and compute demands. We present RMT-KD, a compression method that leverages Random Matrix Theory (RMT) for knowledge distillation to iteratively reduce network size. Instead of pruning or heuristic rank selection, RMT-KD preserves only informative directions identified via the spectral properties of hidden representations. RMT-based causal reduction is applied layer by layer with self-distillation to maintain stability and accuracy. On GLUE and CIFAR-10, RMT-KD achieves up to 80% parameter reduction with only 2% accuracy loss, delivering 2.8x faster inference and nearly halved power consumption. These results establish RMT-KD as a mathematically grounded approach to network distillation.

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Davide Ettori, Nastaran Darabi, Sureshkumar Senthilkumar, Amit Ranjan Trivedi. 2025-09-19. RMT-KD: Random Matrix Theoretic Causal Knowledge Distillation. https://arxiv.org/abs/2509.15724

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