Reasoning with Sampling: Cutting at Decision Points

Frontier reasoning models are produced by post-training base language models with reinforcement learning. Recent work has challenged this by showing that sampling from a sharpened version of the base model's distribution, a so-called power distribution, elicits comparable reasoning without additional training, curated datasets, or verifiers. However, making this method practical requires efficiently sampling from the power distribution. A sampler needs to "mix" to the power distribution which necessitates moving between modes of the target distribution; intuitively, e.g., trying different reasoning strategies. The samplers proposed in prior works repeatedly select a "cut" position in the current reasoning trace uniformly at random and resamples the suffix from that position onwards. However, reasoning traces typically contain a few consequential decisions (e.g., the choice of proof strategy or algorithm), and we observe that a uniformly chosen cut tends to rewrites local details rather than revisiting decision points. We introduce an algorithm (Entropy-Cut Metropolis--Hastings) which uses the base model's next-token entropy as a proxy to identify key decision points and re-sample from these positions. We empirically verify that entropy jumps are a useful proxy for decision points, and in a stylized model of reasoning prove that our method's mixing time scales with the number of decisions in a trace rather than with the number of tokens, which can be much larger. Across MATH500, HumanEval, GPQA, and AIME on several models, our method consistently improves over baselines and RL-trained models.