新闻
Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- SGUID method selects which skills to distill into language models, finding that fewer than 25% of retrieved skills provide useful training signals.
- 为何重要
- Matters for engineers optimizing LLM training pipelines who use skill distillation to improve model performance on downstream tasks.
- 注意
- Results shown on specific model families (Olmo, Qwen); generalization to other architectures and whether skill selection overhead justifies gains remains unclear.
- llm
- distill
- inference
这条新闻背后的模式
- Agentic Context Engineering (Evolving Playbook)
- Evolutionary Discovery Algorithms
- Cost-Aware Model Selection
每个模式都讲清楚技术如何运作、何时值得投入，以及在哪里会失效。
The Agent Architect
每周一个模式、一个权衡、一个生产事故案例。为构建智能体系统的人准备的每周简报。
每周一封邮件,一键退订。您的地址仅用于发送简报。