Title: CarBoN: Calibrated Best-of-N Sampling Improves Test-time Reasoning
Primary area: foundation or frontier models, including LLMs
Keywords: test-time scaling, calibration, LLM

Abstract: Allocating more computation during inference time (test-time scaling) improves language model performance, especially for reasoning tasks.
However, popular methods like Best-of-$N$ sampling often show diminishing returns as $N$ increases.
To address this inefficiency, we introduce a general $\textbf{test-time calibration framework}$ that adaptively modifies the model toward high-reward reasoning paths, with theoretical guarantees of improving the lower bound of expected reward under finite sampling, all without large language model (LLM) retraining.
Within this framework, we propose $\textbf{CarBoN}$ (Calibrated Best-of-$N$), a two-phase method that first explores the solution space and then learns a calibration of the logits via an input-specific temperature $T$ and additive shift vector $\delta$, guiding generation toward more reliable reasoning.
Experiments on MATH-500 and AIME-2024 show that CarBoN improves efficiency, with up to $4\times$ fewer rollouts to reach the same accuracy, while often achieving higher accuracy under fixed budgets.
We also analyze the complementary roles of $T$ and $\delta$ in balancing output diversity and correctness, and demonstrate that the framework also generalizes to step-level sampling strategies such as beam search.