Structured Transforms for Low-Overhead Quantization of Language Models
We revisit Kashin-decomposition-based weight quantization for large language models and propose an improved algorithm with stronger convergence properties and structured, efficient orthogonal transforms. The method retains the core factorization of each weight into two components -- one with bounded infinity norm and the other with bounded infinity norm after an orthogonal transformation -- but replaces the dense random orthogonal matrix with a sign-randomized Discrete Cosine Transform (DCT), reducing the per-iteration cost from $\mathcal{O}(N^2)$ to $\mathcal{O}(N \log N)$. The proposed greedy algorithm with alternating updates guarantees the four-peak distribution required for stable 2-...
| Authors | Daria Cherniuk, Alexander Rudikov, Boris Kashin et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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