ReASearch：推理驱动的统一优化框架
原标题：The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows
AI 摘要
ReASearch框架提出了一种统一的推理驱动优化方法，让单个工具使用代理自主决定评估内容、诊断失败、进行编辑和验证重启，而非依赖外部循环控制器。在14个不同任务中，ReASearch与专业优化系统相比具有竞争力且多数情况下表现更好，性能提升2%至40%，有时甚至超越人类已知最佳结果。该框架通过共享代理循环和领域特定工具，可同时优化提示词、程序和机器学习工作流。
正文节选
The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows Abstract Recent systems for optimizing prompts, programs, and ML workflows typically rely on explicit outer-loop controllers such as evolutionary search, bandits, or textual-gradient methods. We ask a fundamentally different question: how much of this search policy can be internalized by a single tool-using agent? We present ReASearch, a unified framework for reasoning-driven optimization in which the