Calibrated Reasoning: An Explanatory Verifier for Dynamic and Efficient Problem-Solving
By: Anisha Garg , Engin Tekin , Yash More and more
Potential Business Impact:
Helps computers check their own answers better.
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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