简单研究循环：用调参框架做爬山式优化
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Saravia 提醒你：不训练模型也能用简单循环做爬山优化，做 AI 产品前值得搞清楚这点。
Elvis Saravia（@omarsar0）在 X 上讨论一种简单研究循环，即用调参框架做爬山式优化，不涉及训练模型本身。他指出很多人尚未意识到这类方法的影响。他认为无论是研究实验室还是任何在产品中使用 AI 的公司，如果计划参与递归自我改进（RSI）的世界，都难以让模型产出有用结果。
原文 · elvis
By the way, the research that I am talking about is simple research loops (tuning harnesses) for hill climbing. It doesn't even try to train a model or anything. I don't think people realize the implications of this yet. If you plan to participate in a world of RSI, you'll struggle to use these models for anything useful. And this doesn't only apply to research labs; it applies to any business using AI in its products. 💬 1 🔄 0 ❤️ 5 👀 966 📊 2 ⚡
