@inproceedings{yu-etal-2025-z1,
    title = "Z1: Efficient Test-time Scaling with Code",
    author = "Yu, Zhaojian  and
      Wu, Yinghao  and
      Zhao, Yilun  and
      Cohan, Arman  and
      Zhang, Xiao-Ping",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.182/",
    pages = "2688--2712",
    ISBN = "979-8-89176-333-3",
    abstract = "Large Language Models (LLMs) can achieve enhanced complex problem-solving through test-time computing scaling, yet this often entails longer contexts and numerous reasoning token costs. In this paper, we propose an efficient test-time scaling method that trains LLMs on code-related reasoning trajectories, facilitating their reduction of excess thinking tokens while maintaining performance.First, we create Z1-Code-Reasoning-107K, a curated dataset of simple and complex coding problems paired with their short and long solution trajectories. Second, we present a novel Shifted Thinking Window to mitigate overthinking overhead by removing context-delimiting tags (e.g., {\ensuremath{<}}think{\ensuremath{>}}...{\ensuremath{<}}/think{\ensuremath{>}}) and capping reasoning tokens. Trained with long and short trajectory data and equipped with Shifted Thinking Window, our model, Z1-7B, demonstrates the ability to adjust its reasoning level as the complexity of problems and exhibits efficient test-time scaling across different reasoning tasks that matches R1-Distill-Qwen-7B performance with about 30{\%} of its average thinking tokens.Notably, fine-tuned with only code trajectories, Z1-7B demonstrates generalization to broader reasoning tasks (47.5{\%} on GPQA Diamond). Our analysis of efficient reasoning elicitation also provides valuable insights for future research."
}