WizardMath: Empowering Mathematical Reasoning for Large Language Models
  via Reinforced Evol-Instruct
Haipeng Luo Qingfeng Sun Can Xu Pu Zhao Jianguang Lou Chongyang Tao Xiubo Geng Qingwei Lin Shifeng Chen Dongmei Zhang
Large language models (LLMs), such as GPT-4, have shown remarkable
performance in natural language processing (NLP) tasks, including challenging
mathematical reasoning. However, most existing open-source models are only
pre-trained on large-scale internet data and without math-related optimization.
In this paper, we present WizardMath, which enhances the mathematical reasoning
abilities of Llama-2, by applying our proposed Reinforcement Learning from
Evol-Instruct Feedback (RLEIF) method to the domain of math. Through extensive
experiments on two mathematical reasoning benchmarks, namely GSM8k and MATH, we
reveal the extraordinary capabilities of our model. WizardMath surpasses all
other open-source LLMs by a substantial margin. Furthermore, our model even
outperforms ChatGPT-3.5, Claude Instant-1, PaLM-2 and Minerva on GSM8k,
simultaneously surpasses Text-davinci-002, PaLM-1 and GPT-3 on MATH. More
details and model weights are public at https://github.com/nlpxucan/WizardLM
and https://huggingface.co/WizardLM.