MIT 研究用 GPT-6 Astra 直接改权重提升 Qwen3.5-0.8B 数学成绩
不训练也能改模型：Astra 找到让 Qwen3.5-0.8B 复读循环的信号并从权重里减掉，0.016% 的改动让 MATH-500 从 7.9% 涨到 48.1%。
MIT 研究人员让 GPT-6 Astra 在不训练的情况下直接编辑 Qwen3.5-0.8B 的权重。Astra 在 Python notebook 中拿到权重，并用 50 次测试预算发现模型会循环重复直到长度上限，随后追踪到 2 个层写入的单一内部信号并将其从权重中减去。这次编辑只改动 0.016% 的权重，使模型在 MATH-500 上的成绩从 7.9% 升到 48.1%。
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%.
