新闻
Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers developed an on-policy correction method that lets smaller models match stronger models' performance on enterprise tasks by co-evolving system prompts and fine-tuning together.
- 为何重要
- Teams building cost-effective AI agents for domain-specific enterprise work need better ways to adapt smaller models without expensive frontier model inference.
- 注意
- The method was tested on seven enterprise tasks with specific model pairs; generalization to other domains, model sizes, or task types remains unclear.
- agent
- agentic
- prompt
- fine-tun
这条新闻背后的模式
- Agentic Context Engineering (Evolving Playbook)
- Corrective RAG (CRAG)
- Eval-Driven Development (Agent CI)
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。
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