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Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
Feb. 20, 2024, 5:45 a.m. | Peiyi Wang, Lei Li, Zhihong Shao, R. X. Xu, Damai Dai, Yifei Li, Deli Chen, Y. Wu, Zhifang Sui
cs.LG updates on arXiv.org arxiv.org
Abstract: In this paper, we present an innovative process-oriented math process reward model called \textbf{Math-Shepherd}, which assigns a reward score to each step of math problem solutions. The training of Math-Shepherd is achieved using automatically constructed process-wise supervision data, breaking the bottleneck of heavy reliance on manual annotation in existing work. We explore the effectiveness of Math-Shepherd in two scenarios: 1) \textit{Verification}: Math-Shepherd is utilized for reranking multiple outputs generated by Large Language Models (LLMs); 2) …
abstract annotations arxiv breaking cs.ai cs.cl cs.lg data human llms math paper process reinforce reliance reward model solutions step-by-step supervision training type verify wise
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