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In this paper, we present an innovative process-oriented math process rewardmodel called \textbf{Math-Shepherd}, which assigns a reward score to each stepof math problem solutions. The training of Math-Shepherd is achieved usingautomatically constructed process-wise supervision data, breaking thebottleneck of heavy reliance on manual annotation in existing work. We explorethe effectiveness of Math-Shepherd in two scenarios: 1) \textit{Verification}:Math-Shepherd is utilized for reranking multiple outputs generated by LargeLanguage Models (LLMs); 2) \textit{Reinforcement Learning}: Math-Shepherd isemployed to reinforce LLMs with step-by-step Proximal Policy Optimization(PPO). With Math-Shepherd, a series of open-source LLMs demonstratesexceptional performance. For instance, the step-by-step PPO with Math-Shepherdsignificantly improves the accuracy of Mistral-7B (77.9\%$\to$84.1\% on GSM8Kand 28.6\%$\to$33.0\% on MATH). The accuracy can be further enhanced to 89.1\%and 43.5\% on GSM8K and MATH with the verification of Math-Shepherd,respectively. We believe that automatic process supervision holds significantpotential for the future evolution of LLMs.

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations