# 自我改进的AI是件大事！

- 来源：elvis (@omarsar0)
- 发布时间：2026-05-20 23:20
- AIHOT 分数：73
- AIHOT 链接：https://aihot.virxact.com/items/cmpe81pf70a9oslk19ivob2bp
- 原文链接：https://x.com/omarsar0/status/2057119309151817877

## AI 摘要

作者探索利用Fireworks AI Agent，通过自然语言交互自动化完成大语言模型的微调流程。他以Qwen小模型为例，调整其输出风格以优化PaperWiki项目的扩展效率。这一方法灵感源于@karpathy关于LLM知识库的推文，强调微调是让模型更“懂”数据的关键步骤。核心观点是自动化微调可推动构建可递归自我改进的AI系统，最终目标是打造一个能自我优化、用于知识发现和端到端自动化研究的强大工具。

## 正文

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 （https://x.com/omarsar0/status/2042286186920550498?s=20）.

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.

### 引用推文

> elvis：http://x.com/i/article/2056851733582880768