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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.
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  Original paper link: https://arxiv.org/pdf/2412.17395
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-  I have also published the training data constructed during my reproduction of the paper in another repository: https://huggingface.co/datasets/HuggingMicah/warrior_reproduce
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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.
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  Original paper link: https://arxiv.org/pdf/2412.17395
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+  I have also published the training data constructed during my reproduction of the paper in another repository: https://huggingface.co/datasets/HuggingMicah/warrior_reproduce
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+ Also, I reproduced the experimental results in the paper. There are some differences from the original results, and I have marked the distinctions with underlines.
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+ | Models | Matplotlib (155) | NumPy (220) | Pandas (291) | PyTorch (68) | SciPy (106) | Sklearn (115) | TensorFlow (45) | Overall (1000) |
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+ | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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+ | INCODER (6.7B) | 28.3 | 4.4 | 3.1 | 4.4 | 2.8 | 2.8 | 3.8 | 7.4 |
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+ | CodeGen-Mono (16B) | 31.7 | 10.9 | 3.4 | 7.0 | 9.0 | 10.8 | 15.2 | 11.7 |
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+ | Code-Cushman-001 | 40.7 | 21.8 | 7.9 | 12.4 | 11.3 | 18.0 | 12.2 | 18.1 |
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+ | StarCoder (15B) | 51.7 | 29.7 | 11.4 | 21.4 | 20.2 | 29.5 | 24.5 | 26.0 |
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+ | WizardCoder-SC (15B) | 55.2 | 33.6 | 16.7 | 26.2 | 24.2 | 24.9 | 26.7 | 29.2 |
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+ | CodeLlama-Python (6.7B) | 55.3 | 34.5 | 16.4 | 19.9 | 22.3 | 17.6 | 28.5 | 28.0 |
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+ | WizardCoder-CL (6.7B) | 53.5 | 34.4 | 15.2 | 25.7 | 21.0 | 24.5 | 28.9 | 28.4 |
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+ | Magicoder-CL (6.7B) | 54.6 | 34.8 | 19.0 | 24.7 | 25.0 | 22.6 | 28.9 | 29.9 |
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+ | MagicoderS-CL (6.7B) | 55.9 | 40.6 | 28.4 | 40.4 | 28.8 | 35.8 | 37.6 | 37.5 |
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+ | WarriorCoder_published_in_paper (6.7B) | 55.5 | 41.8 | 26.1 | 41.2 | 33.0 | 39.1 | 42.2 | 38.1 |
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+ | WarriorCoder_my_reproduce (6.7B) | 56.1 | 45.0 | 32.0 | 38.2 | 36.8 | 44.3 | 48.9 | 41.7 |