RLTS studies the internal economics of large reasoning models. What a model genuinely deliberates, what it merely rehearses, and what it can afford to forget.
We think the current generation of reasoning models is wildly inefficient with its own tokens, and that most of the gap between a frontier model and a small one lives in deliberation, not knowledge.
Our work is aimed at closing that gap with methods, not scale.