Venue
International Conference on Learning Representations
Domain
Artificial intelligence
In recent years, large language models have greatly improved in their ability to perform complex multi-step reasoning. However, even stateof-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.
The paper investigates the efficacy of two supervision methods for training reward models in large language models: outcome supervision (ORM) and process supervision (PRM). It emphasizes that while current language models show improved multi-step reasoning, they often generate incorrect results. The authors find that process supervision significantly enhances performance on the MATH dataset, achieving a 78.2% success rate for solving problems from a subset of this dataset. They also introduce the PRM800K dataset, consisting of 800,000 step-level human feedback labels, which supports the training of their reward model. Active learning strategies further enhance the effectiveness of process supervision by selecting the most informative examples for human labeling. The paper concludes that process supervision not only improves model reliability but also leads to safer, more interpretable reasoning in AI systems.
This paper employs the following methods:
- Outcome Supervision
- Process Supervision
The following datasets were used in this research:
- Process supervision achieves 78.2% problem-solving accuracy on MATH test set.
- Active learning improves data efficiency by 2.6x.
The authors identified the following limitations:
- Potential test set contamination due to overlap with pre-training data.
- Possible issues in manual labeling processes.
- Number of GPUs: None specified
- GPU Type: None specified
large language models
multi-step reasoning
reward models
process supervision
outcome supervision
active learning
math dataset