Arguments = "Autoregressive models, which generate each solution token by token, have no mechanism to correct their own errors. We address this problem by generating 100 candidate solutions and then select the solution that is ranked highest by the verifier which are trained to evaluate the correctness of model-generated solutions. the verifier decides which ones, if any, are correct. Verifiers benefit from this inherent optionality, as well as from the fact that verification is often a simpler task than generation."

This section of the text could be strengthened by referencing and elaborating on the specific examples mentioned. For example, it would be valuable to provide examples that support the notion that autoregressive models have no mechanism to correct their own errors. Additionally, it would be valuable to provide examples that illustrate how the authors overcame this shortcoming by generating 100 candidate solutions and selecting the solution that is ranked highest by the verifier. Additionally, it would be valuable to provide examples that explain why this approach can be considered an inherent optionality, as well as examples that describe the benefits that this provides to the verifier.

Additionally, inconsistencies in the text may require some rewording. For example, the text could be clearer when defining the verifier, which decides which ones, if any, are correct. In particular, it is important to make clear that the verifier is not evaluating the correctness of the generated solutions, but only the correctness of the model-generated solutions.

Finally, it is important to identify and correct any logical fallacies, contradictory statements, or gaps in reasoning. For example, it is important to clarify that the verifier is not only evaluating the correctness of the model-generated solutions, but also the correctness of the verifier-generated solutions.