Interpretable Adaptive Sampling for LLM Test-Time Scaling

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Interpretable Adaptive Sampling for LLM Test-Time Scaling

Test-time scaling improves LLM reasoning by generating and aggregating multiple candidate answers, yet many pipelines use fixed per-query budgets that spend the same compute on easy and difficult prompts. These fixed budgets are also difficult to inspect because they do not explain why a given prompt receives a particular number of samples. We propose adaptive} test-time scaling with a lightweight fuzzy controller that maps interpretable signals, including estimated prompt complexity and model c

Source: ArXiv AI