New Delhi, Oct. 20 -- In June last year, researchers at the Massachusetts Institute of Technology published a paper describing a process by which large language models could generate training notes and use them to update their own weights.

A one-billion-parameter model that could not solve a single puzzle in the test set managed to solve over 70% of them by using this technique. In a separate test, the notes a larger model wrote for itself proved better training material than those generated by GPT-4.1.

Today, AI models are trained by adjusting their weights on vast amounts of data until they perform as intended. The weights are then frozen, and the model released. From that point onwards, the model weights remain static-nothing that it...