← 研踪 PaperTrailarXiv:2602.15200 · v3
COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression
首次提交 2026-02-17 05:31
版本更新 2026-10-27 20:22
雷达观测 2026-10-29 00:35
摘要
Post-training compression of transformer models commonly relies on truncated singular value decomposition (SVD) strategies leading to low-rank approximations of the original model weights. However, the underlying single shared subspace modeling can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union-of-subspaces representation, but existing approaches often suffer from costly iterative dictionary and coefficient updates. We propose $\textbf{COMPOT}$ ($\textbf{C}$alibration-$\textbf{O}$ptimized $\textbf{M}$atrix $\textbf{P}$rocrustes $\textbf{O}$rthogonalization for $\textbf{T}$ransformers), a training-free compression framework that uses a small calibration dataset to estimate a sparse weight factorization. COMPOT employs orthogonal dictionaries that enable closed-form Procrustes updates for the dictionary and analytical single-step sparse coding for the coefficients, reducing significantly the overall optimization cost. To handle heterogeneous layer sensitivity under a global compression budget, COMPOT further introduces a one-shot dynamic allocation strategy that adaptively redistributes layer-wise compression rates. Extensive experiments across diverse architectures and tasks show that COMPOT consistently delivers a superior quality-compression trade-off over strong low-rank and sparse baselines, while remaining fully compatible with post-training quantization for extreme compression.
作者备注：Paper was accepted to NeurIPS 2026
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v3 · 2026-10-27 20:22 · 最新观测版本
COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression
Post-training compression of transformer models commonly relies on truncated singular value decomposition (SVD) strategies leading to low-rank approximations of the original model weights. However, the underlying single shared subspace modeling can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union-of-subspaces representation, but existing approaches often suffer from costly iterative dictionary and coefficient updates. We propose $\textbf{COMPOT}$ ($\textbf{C}$alibration-$\textbf{O}$ptimized $\textbf{M}$atrix $\textbf{P}$rocrustes $\textbf{O}$rthogonalization for $\textbf{T}$ransformers), a training-free compression framework that uses a small calibration dataset to estimate a sparse weight factorization. COMPOT employs orthogonal dictionaries that enable closed-form Procrustes updates for the dictionary and analytical single-step sparse coding for the coefficients, reducing significantly the overall optimization cost. To handle heterogeneous layer sensitivity under a global compression budget, COMPOT further introduces a one-shot dynamic allocation strategy that adaptively redistributes layer-wise compression rates. Extensive experiments across diverse architectures and tasks show that COMPOT consistently delivers a superior quality-compression trade-off over strong low-rank and sparse baselines, while remaining fully compatible with post-training quantization for extreme compression.
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