Let's Verify Step by Step

TL;DR

This paper compares outcome and process supervision for training large language models in multi-step reasoning, finding process supervision more effective and demonstrating the benefits of active learning and releasing a large feedback dataset.

Contribution

It provides a comprehensive comparison of outcome versus process supervision, showing process supervision's superiority in training models on the MATH dataset, and introduces a large human feedback dataset.

Findings

Process supervision outperforms outcome supervision on MATH dataset.

Active learning enhances process supervision effectiveness.

Released PRM800K dataset of human feedback labels.

Abstract

In recent years, large language models have greatly improved in their ability to perform complex multi-step reasoning. However, even state-of-the-art models still regularly produce logical mistakes. To train more reliable models, we can turn either to outcome supervision, which provides feedback for a final result, or process supervision, which provides feedback for each intermediate reasoning step. Given the importance of training reliable models, and given the high cost of human feedback, it is important to carefully compare the both methods. Recent work has already begun this comparison, but many questions still remain. We conduct our own investigation, finding that process supervision significantly outperforms outcome supervision for training models to solve problems from the challenging MATH dataset. Our process-supervised model solves 78% of problems from a representative subset of the MATH test set. Additionally, we show that active learning significantly improves the efficacy of process supervision. To support related research, we also release PRM800K, the complete dataset of 800,000 step-level human feedback labels used to train our best reward model.