---
id: 20261016-T0-16
title: "逆推理模型：仅用答案数据就能完成LLM后训练与验证"
title_en: "Inverse Reasoning Models Enable Post-Training from Answer-Only Data"
url: https://ai.daily.yangsir.net/daily/20261016-T0-16
issue_date: 2026-10-16
publish_date: 2026-10-15T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2610.18878
---

# 逆推理模型：仅用答案数据就能完成LLM后训练与验证

一篇新论文提出逆推理模型，用于解决LLM后训练中监督成本与计算效率之间的权衡问题。传统监督微调需要推理轨迹数据，但很多场景只有问题和答案。该方法利用仅含答案的数据进行后训练和验证，无需完整的推理过程标注。这为数据稀缺场景下的推理能力训练提供了新思路，可降低对人工标注推理链的依赖。

## English Version

**Inverse Reasoning Models Enable Post-Training from Answer-Only Data**

A new paper proposes Inverse Reasoning Models to address the tradeoff between supervision cost and compute efficiency in LLM post-training. Traditional supervised fine-tuning requires reasoning traces, but many applications only have prompt-answer pairs. This method enables post-training and verification from answer-only data, eliminating the need for full reasoning process annotations. It offers a new approach for training reasoning capabilities in data-scarce scenarios, reducing reliance on manually annotated reasoning chains.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2610.18878)

**详情页**：https://ai.daily.yangsir.net/daily/20261016-T0-16

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