---
id: 20260814-T0-07
title: "零训练调参：新方法Weightless Fine-Tuning实现LLM轻量化个性化"
title_en: "Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport"
url: https://ai.daily.yangsir.net/daily/20260814-T0-07
issue_date: 2026-08-14
publish_date: 2026-08-13T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2608.11342
---

# 零训练调参：新方法Weightless Fine-Tuning实现LLM轻量化个性化

arXiv新论文提出Weightless Fine-Tuning方法，通过在logit空间进行传输，无需为每个用户调整权重即可实现LLM个性化。传统监督微调（SFT）在个性化场景下需要为每位用户单独保存权重、优化和重训，成本高昂。新方法避免了这些开销，使多用户场景下的模型适配更加高效，适合内容推荐和写作风格定制等应用。

## English Version

**Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport**

An arXiv paper introduces Weightless Fine-Tuning, a technique that personalizes LLMs via logit-space transport without per-user weight adjustments. Unlike supervised fine-tuning, which requires separate optimization, storage, and retraining for each author, this method eliminates overhead, enabling efficient adaptation in multi-user settings like content recommendation and writing style customization.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2608.11342)

**详情页**：https://ai.daily.yangsir.net/daily/20260814-T0-07

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