MIT 研究人员让 GPT-6 Astra 直接编辑 Qwen3.5-0.8B 的权重来提升数学能力，无需梯度或训练。Astra 在 Python notebook 中拿到权重，并用 50 次练习题测试预算，发现模型会重复输出直到撞上长度上限，将这一习惯追溯到 2 个层写入的单一内部信号，并从权重中减去该信号。
MASSIVE. MIT researchers showed that GPT-6 Astra can improve another AI model by changing its weights directly, without training.
Here, GPT-6 Astra raised Qwen3.5-0.8B from 7.9% to 48.1% on MATH-500 by editing 0.016% of its weights, without training.
Astra got Qwen’s weights in a Python notebook, plus a budget of 50 test runs on practice math problems. It saw the model kept repeating itself until it hit its length limit, traced that habit to a single internal signal written by 2 of its layers, and subtracted that signal out of the weights.
With the loop gone, the model could finish answers it was already capable of solving.
That single targeted edit changed 0.016% of the weights and lifted math accuracy from 7.9% to 48.1%.
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：Rohan Paul · x.com