全部
全部

选一篇,慢慢读

从左侧挑一篇,正文会在这里排成书页。

文章与公开阅读资产

A-RSI: Adaptive Task Refresh for Zero-Data Multimodal Self-Improvement

Recursive self-improvement requires models not only to acquire useful training experience, but also to decide when their current experience has ceased to be useful. Existing methods often focus on what experience to generate next, while paying less attention to when fresh experience should be introduced. Many also cons

阅读原文 · Yu Liu, Youxing Li, Yuedong Tan, Zhiwei Yang, Zihan Su, Hanqi Feng, Shaojie Zhang, Libin Liao, Zihao Wan, Chenxu Wu, Cong Cao, Fangfang Yuan, Yanbing Liu, Ziheng Chen, Pei Fu, Radu Timofte, Zongwei Wu, Nicu Sebe, Barnabas Poczos, Jian Luan

乔木风格重写

向阳乔木 · opencode-go/deepseek-v4.1-flash

这篇论文讨论的问题挺根本:模型自我改进时,除了“生成什么新经验”,还要判断“旧经验什么时候已经没用了”。 A-RSI 把这件事拆成两个独立问题: 什么时候该换任务 ,以及 新任务的答案怎么保证可查 。前者靠训练日志里的奖励变化和准确率来判断,后者靠一套模型自己写的程序来出题和给参考答案。 如果你在做后训练、合成数据或者多模态 Agent 的自进化流程,这篇值...

  • JK下一篇 / 上一篇
  • S收藏
  • A问 AI
  • I沉浸阅读
  • Esc退出