New ‘Test-Time Training’ method lets AI keep learning without exploding inference costs

A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to digest long docs, tickets, and logs, this is a bid to get “long memory” without paying attention costs that grow with context length.

The approach, called “End-to-End Test-Time Training” (TTT-E2E), reframes language modeling as a continual learning problem: Instead of memorizing facts during pre-training, models learn how to adapt in real time as they process new information.

The result is a Transformer that can match long-context accuracy of full attention models while running at near-RNN efficiency — a potential breakthrough for enterprise workloads where context length is colliding with cost.

The accuracy-efficiency trade-off

For developers building AI systems for long-document tasks, the choice of model architecture often involves a painful trade-off between accuracy and efficiency.

On one side are Transformers ...