初始化项目，由ModelHub XC社区提供模型

Model: taki555/Qwen3-0.6B-Art
Source: Original Platform

README.md

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+---
+base_model:
+- Qwen/Qwen3-0.6B
+datasets:
+- taki555/DeepScaleR-Easy
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Qwen3-0.6B-Art
+
+This is the Chain-of-Thought (CoT) efficient version of the [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) model, trained on the [DeepScaleR-Easy](https://huggingface.co/datasets/taki555/DeepScaleR-Easy) dataset. 
+
+This model was introduced in the paper [The Art of Efficient Reasoning: Data, Reward, and Optimization](https://huggingface.co/papers/2602.20945). Check the [Project Page](https://wutaiqiang.github.io/project/Art) for more details.
+
+## Model Description
+Large Language Models (LLMs) consistently benefit from scaled Chain-of-Thought (CoT) reasoning but also suffer from heavy computational overhead. Efficient reasoning aims to incentivize short yet accurate thinking trajectories, typically through reward shaping with Reinforcement Learning (RL). 
+
+This model follows a two-stage training paradigm: length adaptation and reasoning refinement. It is optimized to maintain a sufficient density of positive reward signals while avoiding the "short-is-correct" trap, demonstrating robust and generalized efficient reasoning capabilities.
+
+## 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}
+}
+```