阿斯塔纳时间
AI QAZAQSTANKZ · AI测试版

哈萨克斯坦人工智能与高科技领域的智能门户

Research1 分钟阅读

内容来自原始来源:

arXiv — cs.AI

在 arXiv — cs.AI 阅读全文

本内容来自公开来源。若发布内容影响您的权利 → 申请删除信息

标题为英语Learning More by Training Less: Uncertainty-Guided Progressive Contraction for Efficient Reasoning Post-Training

arXiv:2610.12933v1 Announce Type: new Abstract: Large language models have achieved remarkable advances in complex reasoning. However, scaling reasoning post-training with ever-larger corpora can substantially increase training cost without proportional performance gains. In…

arXiv — cs.AI作者: Suorong Yang, Jiaxin Wang, Jiaming Su, Chentao Yue, Furao Shen, Peng Ye, Soujanya Poria2026年10月12日 (10月12日)
提供 1 种语言版本

AI 摘要:要点

  • arXiv:2610.12933v1 Announce Type: new Abstract: Large language models have achieved remarkable advances in complex reasoning
  • However, scaling reasoning post-training with ever-larger corpora can substantially increase training cost without proportional performance gains
Research
KZ·AI C63A
Инсайт дня

Learning More by Training Less: Uncertainty-Guided Progressive Contraction for Efficient Reasoning Post-Training

标签:#arXiv
Поделиться:

相关文章

返回列表