Weight-Space Programming
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- MIT CSAIL demonstrates LLM weight-space programming
MIT CSAIL researchers introduced Weight-Space Programming, a method that lets an LLM reason about and directly edit neural-network weights without gradients or a training loop. The authors report that GPT-6 Astra improved Qwen3.5-0.8B by editing 0.016% of its weights, raising MATH-500 from 7.9% to 48.1% and tripling IFEval under the same sampling settings. They also report that Astra created small game-playing networks that outperformed networks trained with PPO for 100 million steps.
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