初始化项目，由ModelHub XC社区提供模型

Model: taki555/Qwen3-4B-Instruct-2507-Art
Source: Original Platform

README.md

@@ -0,0 +1,35 @@
+---
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- taki555/DeepScaleR-Easy
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Art-Qwen3-4B-Instruct-2507
+
+This is the CoT (Chain-of-Thought) efficient version of the **Qwen3-4B-Instruct-2507** model, developed as part of the research presented in the paper [The Art of Efficient Reasoning: Data, Reward, and Optimization](https://huggingface.co/papers/2602.20945).
+
+## Model Description
+
+Art-Qwen3-4B is optimized to produce short yet accurate reasoning trajectories. By using reward shaping and Reinforcement Learning (RL), the training process follows a two-stage paradigm: length adaptation and reasoning refinement. This approach aims to provide the benefits of scaled reasoning while minimizing the heavy computational overhead typically associated with long CoT outputs.
+
+The model was trained on the [DeepScaleR-Easy](https://huggingface.co/datasets/taki555/DeepScaleR-Easy) dataset.
+
+- **Project Page:** [https://wutaiqiang.github.io/project/Art](https://wutaiqiang.github.io/project/Art)
+- **Paper:** [The Art of Efficient Reasoning: Data, Reward, and Optimization](https://huggingface.co/papers/2602.20945)
+
+## 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}
+}
+```