---
id: 20261027-T0-09
title: "LLM置信度估计不用额外训练，RL critic已经会了"
title_en: "RL Critic Already Knows LLM Confidence, No Extra Training Needed"
url: https://ai.daily.yangsir.net/daily/20261027-T0-09
issue_date: 2026-10-27
publish_date: 2026-10-26T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2610.31820
---

# LLM置信度估计不用额外训练，RL critic已经会了

可靠的置信度估计是LLM可信部署的前提，但专门训练置信度模块需要额外数据和算力。这篇论文提出一个问题：置信度到底对应哪个预测问题，并发现强化学习中的critic网络已经隐含了可用的置信度信号，无需单独的置信度训练阶段。如果成立，团队可以直接复用RL训练中已有的critic，省掉一轮数据和算力开销，同时给输出配上可用的置信度分数。

## English Version

**RL Critic Already Knows LLM Confidence, No Extra Training Needed**

Reliable confidence estimation is essential for trustworthy LLM deployment, but dedicated confidence training requires extra data and compute. This paper asks what prediction problem confidence actually corresponds to, and finds that the critic network from reinforcement learning already carries a usable confidence signal, eliminating the need for a separate confidence training stage. If it holds up, teams can reuse the critic already produced during RL training, skipping an extra round of data and compute while still attaching usable confidence scores to outputs.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2610.31820)

**详情页**：https://ai.daily.yangsir.net/daily/20261027-T0-09

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