AI 导读
必读报告！他们发现 LLM 在读写神经网络权重方面出奇地擅长。 想想看，这对 RSI 意义重大！ 当前的 RSI 流程侧重于编写代码来启动训练任务。 这项能力可能提供一条更原生的路径，通过权重递归地自我改进模型。
正文
Must-read report! They find that LLMs are surprisingly good at reading and writing neural network weights.
Huge for RSI when you think about it!
Current RSI pipelines focus on writing code to launch training jobs.
This capability could offer a more native path to recursively self-improve models via weights.
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: https://rikiyat.github.io/blog/weight-space-programming/在 X 查看被引用的帖子
来源：elvis · x.com