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Model: taki555/Qwen3-1.7B-Art
Source: Original Platform

README.md

@@ -0,0 +1,34 @@
+---
+base_model:
+- Qwen/Qwen3-1.7B
+datasets:
+- taki555/DeepScaleR-Easy
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Art-Qwen3-1.7B
+
+This model is the Chain-of-Thought (CoT) efficient version of [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B), 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-Qwen3-1.7B is optimized for efficient reasoning, aiming to produce short yet accurate thinking trajectories. It was trained using Reinforcement Learning (RL) with specialized reward shaping on the [DeepScaleR-Easy](https://huggingface.co/datasets/taki555/DeepScaleR-Easy) dataset. The training follows a two-stage paradigm involving length adaptation and reasoning refinement to maintain high accuracy while reducing computational overhead.
+
+- **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)
+- **Base Model:** [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
+
+## 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}
+}
+```