用反思打败强化学习：GEPA 与通用文本优化协议

有人曾问我，随着底层模型能力不断变强，提示词优化的重要性是否会下降？我的观点恰恰相反：模型越强，其指令遵循能力就越敏锐；当你给一个极其聪明的模型提供关于具体任务愈发精确、高质量的指令时，它在解决该任务上的表现就会呈指数级跃升。

people have asked me that oh as models get better the importance of prompt optimization will go down. I argue the opposite which is as models get better they will get better at instruction following and the more precise instruction about your task that you have to give to a very smart model the better that model will be at a uh solving your task

语境：Lakshya Agrawal 驳斥「模型越强提示工程越无用」的流行论调：更聪明的模型具备更精细的上下文理解力，任务指令与引导信息的精确度反而成为拉开性能差距的乘数。

支持 → all-in-satya-nadella-20260915

我们的核心思路是在文本空间中执行反思式优化：不再仅仅依赖 0 或 1 这种粗粒度的标量奖励信号，而是让大语言模型或智能体审视整个执行轨迹，并深入反思其中哪些步骤有效、哪些无效。

Our idea is to perform reflective optimization in text space where instead of only using the zero or one reward signal, we can have a language model or an agent look at the trace of the entire rollout and reflect on what worked in them, what did not work in them.

语境：Lakshya Agrawal 阐释 GEPA 与传统 RL 的本质区别：传统梯度回传抛弃了中间工具调用与报错诊断，而文本反思能直接提取高阶语义诊断信息实现跳跃式改进。

支持 → ai-engineer-lakshya-agrawal-20260926