---
格式版本: 2
标题: "SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features"
原文链接: "https://arxiv.org/abs/2608.10709"
发布日期: "2026-08-11"
发布时间校准状态: "found"
发布时间需复核: "否"
发布时间来源: "rule:local:strict_original_body"
发布时间证据: "**\\[v1\\]** Tue, 11 Aug 2026 09:29:30 UTC (1,097 KB)"
发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body"
发布时间校准置信度: "high"
发布时间候选数量: 18
发布时间严格候选数量: 6
发布时间原页读取状态: "source template page reused from URL open"
发布时间未找到原因: ""
发布时间校准时间: "2026-08-12T11:16:19+08:00"
发布时间仲裁状态: "skipped"
发布时间仲裁尝试次数: 0
发布时间仲裁耗时毫秒: 0
发现时间: "2026-08-12T11:13:54+08:00"
入库时间: "2026-08-12T03:16:19.674Z"
来源平台: "arXiv 学术论文搜索"
搜索渠道: "source_template"
搜索词: "https://arxiv.org/search/?query=deployment&searchtype=all"
匹配关键词:
  - "deployment"
相关厂家:
  []
相关专家:
  []
内容类型: "网页"
抓取工具: "Free Fetch + Defuddle"
清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取"
原始附件:
  []
AI优质: "否"
AI打分: 10
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "论文主题为机器学习量化训练，与超节点/AI Rack/机柜级AI基础设施完全无关，仅命中无关关键词'deployment'。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T11:16:31+08:00"
AI主题相关性: 0
AI来源权威性: 0
AI新颖性: 0
AI技术细节: 0
AI商业部署信号: 0
AI完整性: 10
采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.10709"
---

## Computer Science > Machine Learning

## Title:SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features

Authors:[HyeonJun Lee](https://arxiv.org/search/cs?searchtype=author&query=Lee,+H), [Hyeonsik Jo](https://arxiv.org/search/cs?searchtype=author&query=Jo,+H), [Jinwoo Chung](https://arxiv.org/search/cs?searchtype=author&query=Chung,+J), [Jangho Kim](https://arxiv.org/search/cs?searchtype=author&query=Kim,+J)

[View PDF](https://arxiv.org/pdf/2608.10709) [HTML (experimental)](https://arxiv.org/html/2608.10709v1)

> Abstract:Quantization-Aware Training (QAT) enables the deployment of quantized models with minimal accuracy degradation. However, in practical scenarios, training labels are often unavailable due to privacy, copyright, or cost constraints. Knowledge Distillation (KD) is a common approach to address this challenge, but we observe that prior work combining QAT with KD suffers from a fundamental limitation: during distillation, the range mismatch between the teacher and the quantized student model induces an unattainable residual, resulting in an irreducible lower bound on the distillation loss. Motivated by this observation, we propose SQuaT (Student-Aware Quantized Teacher Features), a label-free QAT framework with KD that theoretically eliminates this lower bound by applying the student's quantization parameters to quantize the teacher's features during distillation. Through comprehensive experiments across diverse settings, we demonstrate that SQuaT consistently outperforms strong baselines, with particularly pronounced gains in extreme low-bit (e.g., 1- and 2-bit) settings. Furthermore, extensive evaluations across various model design choices show that our approach does not rely on specific architectural assumptions, making it broadly applicable across diverse architectures and quantization settings. The source code is available at [this https URL](https://github.com/lcdbsa522/SQuaT).

| Comments: |  |
| --- | --- |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | [arXiv:2608.10709](https://arxiv.org/abs/2608.10709) \[cs.LG\] |
|  | (or [arXiv:2608.10709v1](https://arxiv.org/abs/2608.10709v1) \[cs.LG\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10709](https://doi.org/10.48550/arXiv.2608.10709) |

## Submission history

From: HyeonJun Lee \[[view email](https://arxiv.org/show-email/e684bd8f/2608.10709)\]  
**\[v1\]** Tue, 11 Aug 2026 09:29:30 UTC (1,097 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10709) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
