---
格式版本: 2
标题: "ReRound: Reconstructive Rounding to Resolve Midpoint Ambiguity in Calibration-Free LLM Quantization"
原文链接: "https://arxiv.org/abs/2608.11045"
发布日期: "2026-08-11"
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发现时间: "2026-08-12T11:26:18+08:00"
入库时间: "2026-08-12T03:28:39.055Z"
来源平台: "arXiv 学术论文搜索"
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AI打分: 15
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去重键: "https://arxiv.org/abs/2608.11045"
---

## Computer Science > Machine Learning

## Title:ReRound: Reconstructive Rounding to Resolve Midpoint Ambiguity in Calibration-Free LLM Quantization

Authors:[He-Yen Hsieh](https://arxiv.org/search/cs?searchtype=author&query=Hsieh,+H), [H. T. Kung](https://arxiv.org/search/cs?searchtype=author&query=Kung,+H+T)

[View PDF](https://arxiv.org/pdf/2608.11045) [HTML (experimental)](https://arxiv.org/html/2608.11045v1)

> Abstract:ReRound (Reconstructive Rounding) is a post-training quantization method that addresses the midpoint ambiguity inherent in standard round-to-nearest (RTN) schemes when quantizing weights near the centers of quantization intervals.  
> Starting from a pretrained LLM, ReRound trains a conditional diffusion model to produce continuous reconstructions of low-bit weights for the LLM. These reconstructed weights act as a guidance signal to disambiguate the rounding direction of weights located close to interval midpoints.  
> To integrate this reconstruction-guided rounding with conventional RTN, ReRound introduces a tolerance metric measuring how far the quantized weight (not the final quantized integer) is away from the midpoint: quantized weights within a tolerance region around midpoints are quantized using diffusion-based reconstructions, whereas weights closer to quantization boundaries are quantized with RTN. By sweeping the tolerance parameter, ReRound generates multiple candidate quantized integer weight matrices and selects the de-quantized weight matrix candidate whose leading singular values most closely match those of the original full-precision weights. This selected candidate determines the tolerance parameter ReRound uses.  
> ReRound is particularly effective for smaller LLMs. Across a range of such models, it consistently outperforms standard RTN for 3-bit and 4-bit weight quantization. ReRound achieves superior accuracy compared to an extensive set of calibration-free methods, remains competitive with calibration-dependent approaches, and operates entirely offline, introducing no additional overhead during low-bit inference.  
> The ReRound strategy represents a new approach for low-bit quantization. The method applies to AI models beyond LLMs. This paper focuses on its applications to small LLMs.

| Comments: |  |
| --- | --- |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | [arXiv:2608.11045](https://arxiv.org/abs/2608.11045) \[cs.LG\] |
|  | (or [arXiv:2608.11045v1](https://arxiv.org/abs/2608.11045v1) \[cs.LG\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11045](https://doi.org/10.48550/arXiv.2608.11045) |

## Submission history

From: He-Yen Hsieh \[[view email](https://arxiv.org/show-email/060a7cce/2608.11045)\]  
**\[v1\]** Tue, 11 Aug 2026 15:18:07 UTC (2,088 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11045) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
