TTT-Discover optimizes GPU kernels 2x faster than human experts — by training during inference

Researchers from Stanford, Nvidia, and Together AI have developed a new technique that can discover new solutions to very complex problems. For example, they managed to optimize a critical GPU kernel to run 2x faster than the previous state-of-the-art written by human experts.

Their technique, called “Test-Time Training to Discover” (TTT-Discover), challenges the current paradigm of letting models “think longer” for reasoning problems. TTT-Discover allows the model to continue training during the inference process and update its weights for the problem at hand.

The limits of 'frozen' reasoning

Current enterprise AI strategies often rely on "frozen" models. Whether you use a closed or open reasoning model, the model's parameters are static. When you prompt these models, they search for answers within the fixed manifold of their training data. This works well for problems that resemble what the model has seen before.

However, true discovery problems, like inventing a novel ...