33 lines
1.4 KiB
Markdown
33 lines
1.4 KiB
Markdown
---
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base_model:
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- Qwen/Qwen3-4B-Thinking-2507
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datasets:
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- taki555/DeepScaleR-Easy
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Art-Qwen3-4B-Thinking-2507
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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).
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The model was trained on the [DeepScaleR-Easy](https://huggingface.co/datasets/taki555/DeepScaleR-Easy) dataset to incentivize short yet accurate thinking trajectories.
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## Model Description
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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.
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For more details, please visit the [Project Page](https://wutaiqiang.github.io/project/Art).
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## Citation
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```bibtex
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@inproceedings{wu2026art,
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title={The Art of Efficient Reasoning: Data, Reward, and Optimization},
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author={Taiqiang Wu and Zenan Xu and Bo Zhou and Ngai Wong},
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year={2026},
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url={https://arxiv.org/pdf/2602.20945}
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}
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``` |