Ethan Mollick shares research on GPT-6 Astra directly rewriting model weights
Ethan Mollick shared a study by Rikiya Takehi and said he is waiting for replication, but considers it a big deal. The study claims that having GPT-6 Astra directly modify the weights of Qwen3.5-0.8B to improve its math ability, changing only 0.016% of the weights, fixed the model's reasoning loops, improving math ability by up to 6x and instruction following by 3x, all without using gradients or training; the authors call this Weight-Space Programming.
View original postWe 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://t.co/bOWCcFhCVq
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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://t.co/bOWCcFhCVq
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Waiting for replication but this is a pretty big deal.