---
id: 20261028-T0-16
title: "Conceptizer：不只预测下一个词，同一语义的多种表达都该被奖励"
title_en: "Conceptizer Rewards All Valid Phrasings Instead of Just the Next Token"
url: https://ai.daily.yangsir.net/daily/20261028-T0-16
issue_date: 2026-10-28
publish_date: 2026-10-27T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2610.33027
---

# Conceptizer：不只预测下一个词，同一语义的多种表达都该被奖励

arXiv新论文提出Conceptizer训练框架，指出传统下一词预测（NTP）只奖励实际出现的那个词，惩罚所有其他选项，但自然语言中同一含义常有多种表达方式。该框架扩展了NTP的训练目标，让语义等价的多种表述都能获得正向信号，从而缓解模型对单一措辞的过拟合。论文编号arXiv:2610.33027v1，属于cs.CL方向。实际影响在于：采用该框架训练的模型在生成任务中可能表现出更强的措辞多样性和鲁棒性，减少对训练语料中特定表达的机械复制。

## English Version

**Conceptizer Rewards All Valid Phrasings Instead of Just the Next Token**

A new arXiv paper introduces Conceptizer, a training framework that challenges standard next-token prediction (NTP). NTP rewards only the token that actually appears and penalizes every alternative, yet natural language often offers many ways to express the same meaning. Conceptizer extends the NTP objective so that semantically equivalent phrasings all receive positive training signal, reducing overfitting to a single wording. The paper is arXiv:2610.33027v1 in cs.CL. In practice, models trained this way may show greater phrasing diversity and robustness in generation tasks, avoiding mechanical reproduction of expressions found in training data.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2610.33027)

**详情页**：https://ai.daily.yangsir.net/daily/20261028-T0-16

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