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

# Art-Qwen3-30B-A3B-Thinking

This repository contains the Chain-of-Thought (CoT) efficient version of the **Qwen3-30B-A3B-Thinking-2507** model, presented in the paper [The Art of Efficient Reasoning: Data, Reward, and Optimization](https://huggingface.co/papers/2602.20945).

The model was trained on the [taki555/DeepScaleR-Easy](https://huggingface.co/datasets/taki555/DeepScaleR-Easy) dataset using Reinforcement Learning (RL) strategies to incentivize accurate yet concise reasoning trajectories, addressing the computational overhead often associated with scaled CoT.

## Resources
- **Paper:** [The Art of Efficient Reasoning: Data, Reward, and Optimization](https://huggingface.co/papers/2602.20945)
- **Project Page:** [https://wutaiqiang.github.io/project/Art](https://wutaiqiang.github.io/project/Art)

## Citation

```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}
}
```