Few-shot learning is a challenging task that requires language models to generalize from limited examples. Large language models like GPT-3 and PaLM have made impressive progress in this area, but they still face difficulties in reasoning tasks such as GSM8K, a benchmark for arithmetic problems. To improve their reasoning skills, previous work has proposed to guide the language model with prompts that elicit a series of reasoning steps before giving the final answer, achieving a significant improvement on GSM8K fromtoin problem-solving rate. In this paper, we presentDiVeRSe(Diverse Verifier on Reasoning Step) , a novel approach that further enhances the reasoning capability of language models.DiVeRSehas three main components: first, it generates diverse prompts to explore different reasoning paths for the same question; second, it uses a verifier to filter out incorrect answers based on a weighted voting scheme; and third, it verifies each reasoning step individually instead of the whole chain. We evaluateDiVeRSeon the latest language modelcode-davinci-002and show that it achieves new state-of-the-art results on six of eight reasoning benchmarks (e.g., GSM8K) .