Adaptive Branching MCTS, a novel inference-time framework for LLMs, generalizes repeated sampling with multi-turn exploration and exploitation.

We exactly characterize the expressive power of transformers with padding tokens as $\mathsf{TC}^0$, and we also characterize transformers with looping and padding.

We show that limiting a model's confidence during training can improve test-time scaling in mathematical reasoning.