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arXiv · 2509.07820

Certainty-Guided Reasoning in Large Language Models: A Dynamic Thinking Budget Approach

João Paulo Nogueira·Wentao Sun·Alonso Silva·Laith Zumot
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Abstract

Large reasoning language models are typically run with fixed inference budgets, which can waste computation or terminate reasoning prematurely. We introduce Certainty-Guided Reasoning (CGR), a model-agnostic adaptive inference procedure that periodically probes whether the current reasoning supports a confident final answer and terminates early once a target certainty threshold is reached, otherwise continuing until the end-of-thinking token or the budget limit. Certainty is estimated from the model's predicted probabilities over the answer tokens, yielding a lightweight stopping criterion. On AIME2025, CGR preserves baseline accuracy while reducing token usage, providing a tunable certainty-efficiency trade-off that can eliminate millions of tokens in aggregate. Across 64 random seeds, CGR exhibits consistent behavior. We also introduce a Grade metric that penalizes incorrect answers and permits abstention, capturing risk-sensitive performance. Results show that CGR improves Grade by abstaining when certainty remains low.

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João Paulo Nogueira, Wentao Sun, Alonso Silva, Laith Zumot. 2025-09-09. Certainty-Guided Reasoning in Large Language Models: A Dynamic Thinking Budget Approach. https://arxiv.org/abs/2509.07820

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