Omar 把 AI 自我改进从概念变成了可实操的流程——用 Fireworks Agent 自动微调模型，做知识管理或研究自动化的团队可以直接复现，省去手动调参的麻烦。
Omar 展示了如何利用 Fireworks AI Agent 自动化 LLM 的后训练微调过程。他通过 Claude Code 与 Fireworks Agent 交互，用自然语言指令微调一个小型 Qwen 模型，使其输出风格适配 PaperWiki 项目。这标志着 AI 系统自我改进的初步探索，未来目标是让模型能递归地自我优化，用于知识发现和端到端研究自动化。
Self-improving AI is a big deal! As a first step, I've been exploring how much of the post-training...
Self-improving AI is a big deal! As a first step, I've been exploring how much of the post-training can be automated. Here is a first post on how I am using @FireworksAI_HQ Agent to automate LLM fine-tuning itself. Dataset + Skill file included. For the use case, I took inspiration from @karpathy 's tweet on LLM Knowledge Bases. I asked Claude Code to interact with Fireworks Agent to fine-tune a small Qwen model to get the right output style to efficiently keep growing my PaperWiki ( x.com/omarsar0/statu… ). All done via natural language. This is obviously the future of improving AI systems. The next step with the PaperWiki project is how to tune a model to better "know" the data. Harder to do, but if possible, then we have an incredibly powerful system that can recursively self-improve and can be extremely useful for things like knowledge discovery and automating all kinds of research end-to-end. More on this soon. Thanks to the Fireworks team for allowing me to test this early. Super excited about this. elvis @omarsar0 x.com/i/article/2056… 🔗 View Quoted Tweet 💬 7 🔄 6 ❤️ 34 👀 7264 📊 13 ⚡