Per-Matrix Optimality Is Not Enough: Three-Level Optimization for Low-Rank LLM Compression
Per-matrix singular value decomposition (SVD) truncation is Eckart-Young optimal in the whitened Frobenius norm, but errors from independently compressed matrices compound through the block's nonlinear forward pass. Inspired in part by hierarchical variational optimization in quantum many-body methods, we introduce a three-level chain that widens optimization scope from individual matrices to Transformer blocks to the full model: whitened SVD~(L1), block-level joint optimization~(L2), and end-to-end language-modeling loss refinement~(L3), all from 256 calibration sequences, with no instruction or recovery data. On LLaMA-7B at 60% compression, the chain reduces WikiText-2 perplexity from 4...
| Authors | Huicheng Zhang, Xiyao Feng, Ze-Tong Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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