---
id: 20260625-T0-09
title: "Spec Learning：无需训练的推理阶段对齐新方案"
title_en: "Spec Learning: Inference-Time Alignment Without Training"
url: https://ai.daily.yangsir.net/daily/20260625-T0-09
issue_date: 2026-06-25
publish_date: 2026-06-24T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2606.24004
---

# Spec Learning：无需训练的推理阶段对齐新方案

研究提出了 Spec Learning 方案，允许在推理阶段直接对齐模型行为，无需重新训练模型权重。该方法通过从偏好对中提炼特征，动态调整模型输出，解决了传统微调成本高、易出错的问题。实验显示，该方法能显著提升模型遵循复杂指令的准确率。

## English Version

**Spec Learning: Inference-Time Alignment Without Training**

The paper proposes Spec Learning, enabling model alignment at inference time without retraining weights. By distilling features from preference pairs, it dynamically adjusts outputs to avoid the high costs and fragility of fine-tuning, significantly improving accuracy in following complex instructions.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2606.24004)

**详情页**：https://ai.daily.yangsir.net/daily/20260625-T0-09

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