新闻
RRSI: Regularized Recursive Self-Improvement of Agent Harnesses
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- RRSI method automates LLM agent harness optimization by iteratively editing prompts, tools, and control flow while using regularization to prevent overfitting to training tasks.
- 为何重要
- Relevant for engineers building LLM-based agents who want better generalization across different task types without manual prompt engineering.
- 注意
- Paper shows 4.7 point gains on out-of-distribution benchmarks versus 14.1 on training tasks, suggesting regularization helps but generalization gaps remain significant.
- agent
- llm
- prompt
- benchmark
- self-improv
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