Certainty-Guided Reasoning in Large Language Models: A Dynamic Thinking Budget Approach
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912889342787584 |
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| author | Nogueira, João Paulo Sun, Wentao Silva, Alonso Zumot, Laith |
| author_facet | Nogueira, João Paulo Sun, Wentao Silva, Alonso Zumot, Laith |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_07820 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Certainty-Guided Reasoning in Large Language Models: A Dynamic Thinking Budget Approach Nogueira, João Paulo Sun, Wentao Silva, Alonso Zumot, Laith Artificial Intelligence Computation and Language 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. |
| title | Certainty-Guided Reasoning in Large Language Models: A Dynamic Thinking Budget Approach |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.07820 |