@inproceedings{du-etal-2025-agentic,
    title = "Agentic-R1: Distilled Dual-Strategy Reasoning",
    author = "Du, Weihua  and
      Aggarwal, Pranjal  and
      Welleck, Sean  and
      Yang, Yiming",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.emnlp-main.604/",
    doi = "10.18653/v1/2025.emnlp-main.604",
    pages = "12040--12054",
    ISBN = "979-8-89176-332-6",
    abstract = "Current long chain-of-thought (long-CoT) models excel at mathematical reasoning but rely on slow and error-prone natural language traces. Tool-augmented agents address arithmetic via code execution, but often falter on complex logical tasks. We introduce a fine-tuning framework, **DualDistill**, that distills complementary reasoning strategies from multiple teachers into a unified student model. Using this approach, we train **Agentic-R1**, which dynamically selects the optimal strategy for each query, invoking tools for arithmetic and algorithmic problems and using text-based reasoning for abstract ones. Our method improves accuracy on computation-intensive tasks and reduces inference latency on standard benchmarks, demonstrating the promise of multi-strategy distillation for robust and efficient reasoning."
}