A fuzzy controller that allocates a per-prompt sampling budget keeps LLM accuracy near a fixed full-budget baseline while reducing the average number of candidate answers on some datasets.
Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting
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Interpretable Adaptive Sampling for LLM Test-Time Scaling
A fuzzy controller that allocates a per-prompt sampling budget keeps LLM accuracy near a fixed full-budget baseline while reducing the average number of candidate answers on some datasets.