"Genetic engineering for neural networks": MIT proposes editing AI model weights without fine-tuning
MIT scientists have proposed a "weight-space programming" method that allows improving AI models by directly editing parameters without fine-tuning. On the MATH-500 benchmark, Qwen3.5-0.8B's result grew from 7.9 to 48.1%, though the method is still experimental and not universal.
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