Calibrated Reasoning: An Explanatory Verifier for Dynamic and Efficient Problem-Solving

• URL: http://arxiv.org/abs/2509.19681v1
• Date: Wed, 24 Sep 2025 01:36:00 GMT
• Title: Calibrated Reasoning: An Explanatory Verifier for Dynamic and Efficient Problem-Solving
• Authors: Anisha Garg, Engin Tekin, Yash More, David Bick, Nishit Neema, Ganesh Venkatesh,
• Abstract summary: We propose a pairwise Explanatory Verifier that produces calibrated confidence scores and associated natural language reasoning for generated solutions.<n>Our verifier improves the accuracy and efficiency of test-time strategies like best-of-n and self-reflection.
• Score: 2.357104785442987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advanced test-time computing strategies are essential for scaling reasoning models, but their effectiveness is capped by the models' poor self-evaluation. We propose a pairwise Explanatory Verifier, trained via reinforcement learning (GRPO), that produces calibrated confidence scores and associated natural language reasoning for generated solutions. Our verifier improves the accuracy and efficiency of test-time strategies like best-of-n and self-reflection. Crucially, it excels at identifying challenging failure modes, such as when both candidate solutions are identically incorrect, succeeding where standard methods like majority voting fail.

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