PRM Trainer ↗

PRM Trainer is an experimental API which is subject to change at any time.

Overview#

Process-supervised Reward Models (PRM) were proposed in Solving math word problems with process- and outcome-based feedback by Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins.

The abstract from the paper is the following:

Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks. When moving beyond prompting, this raises the question of how we should supervise such models: outcome-based approaches which supervise the final result, or process-based approaches which supervise the reasoning process itself? Differences between these approaches might naturally be expected not just in final-answer errors but also in reasoning errors, which can be difficult to detect and are problematic in many real-world domains such as education. We run the first comprehensive comparison between process- and outcome-based approaches trained on a natural language task, GSM8K. We find that pure outcome-based supervision produces similar final-answer error rates with less label supervision. However, for correct reasoning steps we find it necessary to use processbased supervision or supervision from learned reward models that emulate process-based feedback. In total, we improve the previous best results from 16.8% → 12.7% final-answer error and 14.0% → 3.4% reasoning error among final-answer-correct solutions.

This post-training method was contributed by Gaetan Lopez, Lewis Tunstall, Quentin Gallouédec and Agustín Piqueres.

Quick start#

This example demonstrates how to train a model using the PRM method. We use the Qwen 0.5B model as the base model. We use the stepwise supervision data from the Math Shepherd dataset. You can view the data in the dataset here: