Large language models are increasingly used for complex mathematical and scientific reasoning, making verification of their step-by-step logic a pressing concern. A new arXiv paper argues that current approaches to stepwise verification, which rely on large decoder models to generate justifications or checks, have produced mixed results.
The paper, titled 'Stepwise Reasoning Verification: Don't Generate, Classify!', proposes reframing the task as classification rather than generation. Instead of having a model produce a verification output, the method would classify whether a given reasoning step is valid. This shift, the authors suggest, could avoid some of the instability seen in generative verification.
The work targets the growing need for reliable verification in domains where correctness is critical. By moving from generation to classification, the approach may offer a more direct and dependable signal for assessing reasoning quality.