SQuAT: Spherical Lattice Vector Quantization-Aware Training
Vector quantization (VQ), in particular lattice-based methods, offer strong rate-distortion trade-offs for low-bit large language models (LLMs), yet their behavior under quantization-aware training (VQ-QAT) remains poorly understood. We scale lattice VQ-QAT to billion-parameter LLMs and multi-billion-token training regimes, and introduce SQuAT, a shape-gain VQ-QAT approach trained with a separate straight-through estimator (STE) for the shape and gain. SQuAT decomposes weights into direction and magnitude: normalized directions are quantized using an $E_8$-derived spherical codebook, while gains are quantized independently with low-bit scalar quantizers. We show that, unlike a standard ST...
| Authors | Sebastian Loeschcke, Ivan Koryakovskiy, Tycho F. A. van der Ouderaa et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 October 2026 |
| Last updated at source | 22 October 2026 |
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