COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression

• URL: http://arxiv.org/abs/2602.15200v1
• Date: Mon, 16 Feb 2026 21:31:34 GMT
• Title: COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression
• Authors: Denis Makhov, Dmitriy Shopkhoev, Magauiya Zhussip, Ammar Ali, Baher Mohammad, Stamatios Lefkimmiatis,
• Abstract summary: Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD)<n>We propose COMPOT, a training-free compression framework that uses a small calibration dataset to estimate a sparse weight factorization.<n> COMPOT consistently delivers a superior quality-compression trade-off over strong low-rank and sparse baselines.
• Score: 5.280540253822294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD). However, enforcing a single shared subspace can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union-of-subspaces representation, but existing approaches often suffer from iterative dictionary and coefficient updates. We propose COMPOT (Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers), 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, eliminating iterative optimization. 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. Code is available $\href{https://github.com/mts-ai/COMPOT}{here}$.

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