Let's Reinforce Step by Step

• URL: http://arxiv.org/abs/2311.05821v1
• Date: Fri, 10 Nov 2023 01:35:51 GMT
• Title: Let's Reinforce Step by Step
• Authors: Sarah Pan, Vladislav Lialin, Sherin Muckatira, and Anna Rumshisky
• Abstract summary: We use Reinforcement Learning from Human Feedback to shape model reasoning processes. Our results show that the fine-grained reward provided by PRM-based methods enhances accuracy on simple mathematical reasoning. We also show the critical role reward aggregation functions play in model performance.
• Score: 10.65244642965387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While recent advances have boosted LM proficiency in linguistic benchmarks, LMs consistently struggle to reason correctly on complex tasks like mathematics. We turn to Reinforcement Learning from Human Feedback (RLHF) as a method with which to shape model reasoning processes. In particular, we explore two reward schemes, outcome-supervised reward models (ORMs) and process-supervised reward models (PRMs), to optimize for logical reasoning. Our results show that the fine-grained reward provided by PRM-based methods enhances accuracy on simple mathematical reasoning (GSM8K) while, unexpectedly, reducing performance in complex tasks (MATH). Furthermore, we show the critical role reward aggregation functions play in model performance. Providing promising avenues for future research, our study underscores the need for further exploration into fine-grained reward modeling for more reliable language models.

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