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 This is my reproduction of the Microsoft team's work, WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models. It is fully based on open-source models to construct training data and adopt supervised fine-tuning (SFT) to train the model. The results on code generation benchmarks like Humaneval (Humaneval+) and MBPP (MBPP+) are as follows: 79.9 (75.4), 75.8 (64.5). These results are excellent, confirming that the idea of 'learning from expert battles' proposed in the paper has great potential. I have also published the training data constructed during my reproduction of the paper in another repository, and everyone is welcome to use it.
-Original paper link: https://arxiv.org/pdf/2412.17395
+Original paper link: https://arxiv.org/pdf/2412.17395
+ I have also published the training data constructed during my reproduction of the paper in another repository: https://huggingface.co/datasets/HuggingMicah/warrior_reproduce