HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization
Post-training quantization (PTQ) is essential for deploying LLMs under memory and bandwidth constraints. However, extreme low-bit quantization remains highly sensitive to activation outliers and anisotropic weight curvature. Existing incoherence-based PTQ methods mitigate this issue with fixed randomized Hadamard transforms (RHTs), which improve quantization robustness but cannot adapt the rotated basis to the layer, calibration distribution, or quantizer. We introduce HARP (Hadamard-preconditioned Adaptive Rotation Processor), a learnable structured two-sided orthogonal processor that replaces fixed Hadamard mixing while preserving exact full-precision equivalence. HARP represents each r...
| Authors | Artur Zagitov, Gleb Molodtsov, Aleksandr Beznosikov |
|---|---|
| Primary category | cs.LG |
| Published | 28 May 2026 |
| Last updated at source | 2 September 2026 |
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