无需更新权重，通过逻辑空间修正实现大模型个性化。
Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport
- 在推理阶段用逻辑空间扰动模拟微调效果，不修改模型参数。
- 在三个LaMP数据集上平均表现最优，计算成本低于SFT的7%。
- 适合资源受限场景下的个性化需求，如个人写作助手。
监督微调（SFT）是适配大语言模型至目标分布的标准方法，但在个性化等场景中，每个作者需独立进行权重访问、优化、存储与重训练，成本过高。本文提出无权重微调（WFT），一种无需训练的推理阶段方法，可在不更新权重的情况下近似SFT的分布效应。WFT在作者训练序列上计算监督残差，并通过从丢弃诱导的交叉协方差估计出的跨前缀传输算子，将残差传递到当前提示中。该算子捕捉了上下文扰动对预测的传播机制，以逻辑空间修正替代基于梯度的参数更新。在三个LaMP个性化基准测试中，WFT在各数据集上平均表现最佳，单任务上达到或超过SFT性能，且优于其他轻量级基线。在预算控制对比中，仅使用不到7%的有效计算量即逼近SFT性能。逻辑分析显示，WFT引起的逻辑偏移与SFT在95%的下一个词概率质量上的余弦相似度达0.875，表明其有效捕捉了监督适应的分布效应。
原文摘要 · Abstract (English)
Supervised fine-tuning (SFT) is a standard approach for adapting LLMs to a target distribution, but in settings such as personalization, where each author requires separate weight access, optimization, storage, and retraining, its costs become prohibitive. We propose Weightless Fine-Tuning (WFT), a training-free decoding-time method that approximates the distributional effect of SFT without weight updates. WFT computes supervised residuals on an author's training sequence and transports them to the current prompt through a cross-prefix transport operator estimated from dropout-induced cross-covariance. The operator captures how a perturbation at one context propagates to predictions at another, replacing gradient-based parameter updates with logit-space corrections. On three LaMP personalization benchmarks, WFT achieves the best average performance across datasets, matches or exceeds SFT on individual tasks, and outperforms other lightweight baselines on average. In a budget-controlled comparison, WFT approaches SFT performance using less than 7% of the effective computation. Logit-level analysis shows a cosine similarity of 0.875 between the logit shifts induced by WFT and SFT over 95% of the next-token probability mass, suggesting that WFT captures the distributional effect of supervised adaptation without modifying model weights.
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