34 lines
1.2 KiB
Markdown
34 lines
1.2 KiB
Markdown
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---
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base_model:
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- Qwen/Qwen3-30B-A3B-Instruct-2507
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datasets:
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- taki555/DeepScaleR-Easy
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language:
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- en
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license: apache-2.0
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Qwen3-30B-A3B-Art
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This is the Chain-of-Thought (CoT) efficient version of the **Qwen3-30B-A3B-Instruct-2507** model, introduced in the paper [The Art of Efficient Reasoning: Data, Reward, and Optimization](https://huggingface.co/papers/2602.20945).
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The model is designed to generate short yet accurate reasoning trajectories, reducing computational overhead while maintaining high performance. It was trained on the [DeepScaleR-Easy](https://huggingface.co/datasets/taki555/DeepScaleR-Easy) dataset using reward shaping with Reinforcement Learning (RL).
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## Project Resources
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- **Project Page:** [https://wutaiqiang.github.io/project/Art](https://wutaiqiang.github.io/project/Art)
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- **Paper:** [arXiv:2602.20945](https://huggingface.co/papers/2602.20945)
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## Citation
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If you find this work useful, please cite:
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```bibtex
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@inproceedings{wu2026art,
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title={The Art of Efficient Reasoning: Data, Reward, and Optimization},
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author={Taiqiang Wu and Zenan Xu and Bo Zhou and Ngai Wong},
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year={2026},
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url={https://arxiv.org/pdf/2602.20945}
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}
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```
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