初始化项目，由ModelHub XC社区提供模型

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

README.md

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+---
+base_model:
+- Qwen/Qwen3-4B-Thinking-2507
+datasets:
+- taki555/DeepScaleR-Easy
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Art-Qwen3-4B-Thinking-2507
+
+This is the CoT efficient version of the [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-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 [DeepScaleR-Easy](https://huggingface.co/datasets/taki555/DeepScaleR-Easy) dataset to incentivize short yet accurate thinking trajectories.
+
+## Model Description
+Large Language Models (LLMs) consistently benefit from scaled Chain-of-Thought (CoT) reasoning, but also suffer from heavy computational overhead. This model addresses efficient reasoning by using a two-stage training paradigm: length adaptation and reasoning refinement. Through reward shaping with Reinforcement Learning (RL), the model is optimized to maintain high performance across a wide spectrum of token budgets while avoiding the "short-is-correct" trap.
+
+For more details, please visit the [Project Page](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}
+}
+```