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arXiv Computation and Language By Manh Nguyen, Sunil Gupta, Hung Le

Uncertainty-Aware Budget Allocation for Adaptive Test-Time Reasoning

Read the original on arXiv Computation and Language →

The paper introduces Uncertainty-Aware Budget Allocation (UAB), a concave integer optimization framework that reallocates a fixed sampling budget for language model reasoning based on uncertainty estimated from initial samples. In Phase‑1, each question receives a small number of generations, and the disagreement among these answers (vote entropy) signals difficulty. Phase‑2 uses a marginal‑greedy algorithm to allocate the remaining budget, focusing on questions with high initial disagreement, leading to a 2.3% average accuracy improvement over uniform allocation across five open‑weight models and five reasoning benchmarks, with up to 5.5% gains on individual benchmarks, especially in low‑resource settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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