alphaXiv 复现权重空间编程全部 9 个实验并公开智能体轨迹
We replicated all 9 experiments from the original weight-space programming blog.
摘要
alphaXiv 复现了权重空间编程博客中的全部 9 个实验，并公开完整智能体轨迹、样本前向传播激活等实验产物，读者可提问或继续实验。
摘要由模型根据原文撰写，以原文为准。
正文 · 原文
We replicated all 9 experiments from the original weight-space programming blog.
Some interesting findings:
- Experiment 4 (Repair a car policy): Our one-shot edit took the car from 0/1,024 to 1,024/1,024 ramp climbs while editing just 4,813 weights, 21% fewer than the authors
- Experiment 5 (Writing video game policies from scratch): With all weights initialized to 0, GPT-6 Astra wrote a policy for Breakout that scored 309.9 on average, around ~12x higher than the blog
- Experiment 8 (Editing Qwen3.5-0.8B for math): While the authors got to 48.1% on MATH-500 with 50 passes, we could only achieve 34.4%.
We’ve made this a publicly available project where you can see the full agent traces, view experimental artifacts like activations from sample forward passes, ask follow-up questions, and even continue experimenting.
Check out the project at: https://openresearch.sh/p/qgq98UNCo8pK
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：alphaXiv · x.com
引用原文
askalphaxiv. We replicated all 9 experiments from the original weight-space programming blog.[EB/OL]. (2026-11-12)[访问日期]. https://x.com/askalphaxiv/status/2120573496078283191.