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arXiv · 2509.19681

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

Anisha Garg·Engin Tekin·Yash More·David Bick·Nishit Neema·Ganesh Venkatesh
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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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Anisha Garg, Engin Tekin, Yash More, David Bick, Nishit Neema, Ganesh Venkatesh. 2025-09-24. Calibrated Reasoning: An Explanatory Verifier for Dynamic and Efficient Problem-Solving. https://arxiv.org/abs/2509.19681

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