Art-Qwen3-1.7B

This model is the Chain-of-Thought (CoT) efficient version of Qwen3-1.7B, developed as part of the research presented in the paper "The Art of Efficient Reasoning: Data, Reward, and Optimization".

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 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
Project Page: https://wutaiqiang.github.io/project/Art
Base Model: Qwen/Qwen3-1.7B

Citation

@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}
}