AI BriefingGPT-6 Astra Improves Qwen3.5-0.8B Math Performance 6x via Weight Space Programming
·2026.11.03 16:39
Key point
GPT-6 Astra improved the MATH-500 score of Qwen3.5-0.8B to 48.1% by modifying its weights without training.
Details
- First demonstration of 'weight space programming,' a technique where LLMs read and write neural network weights
- Resolved the reasoning loop issue in Qwen3.5-0.8B by modifying just 0.016% of its weights
- MATH-500 score increased from 7.9% to 48.1%, and IFEval score tripled
- Created a small-scale player neural network that outperformed a network trained for 100 million steps with PPO
- Identified a network trained on the Breakout game with 99% confidence by examining only 17,253 weights
This summary was generated automatically by AI. Check the original for the author's claims and context. Copyright belongs to the original author.
Our guide explains how the AI works. Report summary errors, attribution issues, or removal requests via Contact.
Related
MIT CSAIL Research Demonstrates LLMs Can Natively Read and Write Neural Network Weights
Training 1,000 Weights in Gemma 3 270M Unlocks Hidden Sorting and JSON Skills