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task_categories:
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<div align="center">
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# ✨ ArcherCodeR
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<div>
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🏹️ Reinforcement Learning for Smarter Code Reasoning in LLMs 🎯
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</div>
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</div>
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<div>
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<br>
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<div align="center">
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[](https://github.com/wizard-III/ArcherCodeR)
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[](https://huggingface.co/wizardII/ArcherCodeR-1.5B)
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[](https://huggingface.co/datasets/wizardII/ArcherCodeR-Dataset)
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[](https://wandb.ai/wangjkpkucs-peking-university/ArcherCodeR?nw=nwuserwangjkpkucs)
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</div>
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</div>
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## 📖 Overview
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[`Skywork-OR1-RL-Data`](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data) is **a dataset of verifiable, challenging, and diverse math problems (105K) and coding questions (14K)**. This dataset is used to train the **`Skywork-OR1`** (Open Reasoner 1) model series, which consists of powerful math and code reasoning models trained using large-scale rule-based reinforcement learning with carefully designed datasets and training recipes. This series includes two general-purpose reasoning modelsl, **`Skywork-OR1-7B-Preview`** and **`Skywork-OR1-32B-Preview`**, along with a math-specialized model, **`Skywork-OR1-Math-7B`**.
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- **[`Skywork-OR1-Math-7B`](https://huggingface.co/Skywork/Skywork-OR1-Math-7B)** is specifically optimized for mathematical reasoning, scoring **69.8** on AIME24 and **52.3** on AIME25 — well ahead of all models of similar size.
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- **[`Skywork-OR1-32B-Preview`](https://huggingface.co/Skywork/Skywork-OR1-32B-Preview)** delivers the 671B-parameter Deepseek-R1 performance on math tasks (AIME24 and AIME25) and coding tasks (LiveCodeBench).
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- **[`Skywork-OR1-7B-Preview`](https://huggingface.co/Skywork/Skywork-OR1-7B-Preview)** outperforms all similarly sized models in both math and coding scenarios.
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We select, clean, and curate math and coding problems from open-source datasets, including
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- [NuminaMath-1.5](https://huggingface.co/datasets/AI-MO/NuminaMath-1.5)
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- [DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset)
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- [STILL-3-Preview-RL-Data](https://huggingface.co/datasets/RUC-AIBOX/STILL-3-Preview-RL-Data)
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- [Omni-Math](https://huggingface.co/datasets/KbsdJames/Omni-MATH)
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- [AIME problems prior to 2024](https://huggingface.co/datasets/gneubig/aime-1983-2024)
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- [LeetCodeDataset](https://huggingface.co/datasets/newfacade/LeetCodeDataset)
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- [TACO](https://huggingface.co/datasets/BAAI/TACO)
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We conduct **model-aware difficulty estimation** for each problem and model and conduct **rigorous quality assessment prior to training** via both human and LLM-as-a-Judge to ensure training efficiency and effectiveness. We also perform deduplication within the dataset and remove similar problems from AIME 24, AIME 25, and LiveCodeBench to prevent data contamination.
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## Technical Report
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The technical report will be released soon.
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## Citation
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Please cite the following:
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```bibtex
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@misc{archercoder2025,
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title={ArcherCodeR},
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author={Jiakang Wang},
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note={Blog},
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year={2025}
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}
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```
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