# Large LLM edits small model weights to boost math and instruction following

> Original title: This is cool as heck!

- Source: Yuchen Jin
- Published: 2026-11-02T20:29:13.000Z
- Bad Influence: https://badinfluence.news/items/um0qx8wcozxfy5p5z24b7hbjq
- Original: https://x.com/Yuchenj_UW/status/2117353356226379964

## Summary

Yuchen Jin says using a large LLM to directly edit a smaller model's weights could dramatically improve its capabilities, calling it gene editing for neural networks. The linked post reports that GPT-6 Astra changed only 0.016% of the weights in Qwen3.5-0.8B without gradients or training. It says the edits raised math performance up to 6x and instruction following 3x.

## Post

This is cool as heck!

Imagine using a large LLM to directly edit a smaller model's weights and dramatically improve its capabilities.

It's like gene editing for neural networks. Even cooler than distillation!

## Quoted post @rikiyatakehi

> 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/

https://x.com/rikiyatakehi/status/2117313742891327994