Test-time training lets models learn long documents

Stanford and Nvidia developed TTT-E2E, a technique that lets language models update parameters during inference instead of caching every token. On 128k context tasks, the model matches full-attention transformer accuracy while running 2.7x faster, matching linear-attention model speed.