研究发现 LLM 可原生读写神经网络权重

Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights [R]

AI 导读

一项研究称前沿 LLM 能原生读写神经网络权重，GPT-6 Astra 仅凭原始权重即可判断网络的训练目标。该模型在无环境反馈的情况下从零写出小型游戏网络，表现超过用 PPO 训练 1 亿步的网络。

正文

Link: https://rikiyat.github.io/blog/weight-space-programming/

Snippet: “We find that frontier LLMs are surprisingly good at natively reading and writing neural network weights, well past what we’d expect from humans or conventional algorithms. This might be a big deal for both optimization and interpretability.
Just by looking at the raw weights, GPT-6 Astra can tell what a network was trained to do. And without any environment feedback, it has no trouble writing, from scratch, small game-playing networks that outplay networks trained with PPO for 100 million steps.”

Very surprising result, I didn’t expect frontier models to have this capability that has not been optimized for, and it’s something humans cannot do either.

submitted by /u/RobbinDeBank
[link] [留言]