---
base_model:
- Qwen/Qwen3-30B-A3B-Instruct-2507
datasets:
- taki555/DeepScaleR-Easy
language:
- en
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
---

# Qwen3-30B-A3B-Art

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).

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).

## Project Resources
- **Project Page:** [https://wutaiqiang.github.io/project/Art](https://wutaiqiang.github.io/project/Art)
- **Paper:** [arXiv:2602.20945](https://huggingface.co/papers/2602.20945)

## Citation

If you find this work useful, please cite:

```bibtex
@inproceedings{wu2026art,
  title={The Art of Efficient Reasoning: Data, Reward, and Optimization},
  author={Taiqiang Wu and Zenan Xu and Bo Zhou and Ngai Wong},
  year={2026},
  url={https://arxiv.org/pdf/2602.20945}
}
```