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Research PaperResearchia:202604.21064

A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work

Ran Ben-Basat

Abstract

This note clarifies the relationship between the recent TurboQuant work and the earlier DRIVE (NeurIPS 2021) and EDEN (ICML 2022) schemes. DRIVE is a 1-bit quantizer that EDEN extended to any $b>0$ bits per coordinate; we refer to them collectively as EDEN. First, TurboQuant$_{\text{mse}}$ is a special case of EDEN obtained by fixing EDEN's scalar scale parameter to $S=1$. EDEN supports both biased and unbiased quantization, each optimized by a different $S$ (chosen via methods described in th...

Submitted: April 21, 2026Subjects: Machine Learning; Data Science

Description / Details

This note clarifies the relationship between the recent TurboQuant work and the earlier DRIVE (NeurIPS 2021) and EDEN (ICML 2022) schemes. DRIVE is a 1-bit quantizer that EDEN extended to any b>0b>0 bits per coordinate; we refer to them collectively as EDEN. First, TurboQuantmse_{\text{mse}} is a special case of EDEN obtained by fixing EDEN's scalar scale parameter to S=1S=1. EDEN supports both biased and unbiased quantization, each optimized by a different SS (chosen via methods described in the EDEN works). The fixed choice S=1S=1 used by TurboQuant is generally suboptimal, although the optimal SS for biased EDEN converges to 11 as the dimension grows; accordingly TurboQuantmse_{\text{mse}} approaches EDEN's behavior for large dd. Second, TurboQuantprod_{\text{prod}} combines a biased (bβˆ’1)(b-1)-bit EDEN step with an unbiased 1-bit QJL quantization of the residual. It is suboptimal in three ways: (1) its (bβˆ’1)(b-1)-bit step uses the suboptimal S=1S=1; (2) its 1-bit unbiased residual quantization has worse MSE than (unbiased) 1-bit EDEN; (3) chaining a biased (bβˆ’1)(b-1)-bit step with a 1-bit unbiased residual step is inferior to unbiasedly quantizing the input directly with bb-bit EDEN. Third, some of the analysis in the TurboQuant work mirrors that of the EDEN works: both exploit the connection between random rotations and the shifted Beta distribution, use the Lloyd-Max algorithm, and note that Randomized Hadamard Transforms can replace uniform random rotations. Experiments support these claims: biased EDEN (with optimized SS) is more accurate than TurboQuantmse_{\text{mse}}, and unbiased EDEN is markedly more accurate than TurboQuantprod_{\text{prod}}, often by more than a bit (e.g., 2-bit EDEN beats 3-bit TurboQuantprod_{\text{prod}}). We also repeat all accuracy experiments from the TurboQuant paper, showing that EDEN outperforms it in every setup we have tried.


Source: arXiv:2604.18555v1 - http://arxiv.org/abs/2604.18555v1 PDF: https://arxiv.org/pdf/2604.18555v1 Original Link: http://arxiv.org/abs/2604.18555v1

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Date:
Apr 21, 2026
Topic:
Data Science
Area:
Machine Learning
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