arXiv CS
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Decoupled Marginal Sharpening for Training-Free Inference-Time Scaling
We study how to improve LLM reasoning at inference time without updating model weights or using external reward models. Recent reward-free power-sampling methods define the sequence-level power target $π_α(y\mid x)\propto p_0(y\mid x)^α$, where $α>1$, which increases the relative probability of c
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