We’ve Been Measuring AI Reasoning All Wrong. Here’s How to Fix It.

Author(s): Kaushik Rajan

Originally published on Towards AI.

A new research paper reveals how we can teach language models to actually think, not just guess the right answer.

Imagine a math student who consistently aces every test. You’re impressed. But one day, you look over their shoulder and realize they’re using a bizarre, flawed method to solve problems. They’re getting the right answers, but for all the wrong reasons. It’s just a coincidence that their broken logic is producing correct results for these specific questions.

This article discusses a new research paper from Microsoft and Peking University that critiques current measures of AI reasoning ability, particularly the flawed ‘Pass@K’ metric. The researchers propose a novel method called Reinforcement Learning with Verifiable Rewards (RLVR), which emphasizes teaching AI systems to reason correctly, instead of merely guessing answers. They introduce the ‘CoT-Pass@K’ metric, which evaluates not just the correctness of answers but the logical soundness of the steps leading to those answers. Through empirical testing, the authors demonstrate that these new evaluation methods yield significantly improved performance in AI reasoning, outlining implications for building more trustworthy AI systems capable of logical reasoning in real-world applications.

Read the full blog for free on Medium.

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Published via Towards AI