Agentic-R1: Distilled Dual-Strategy Reasoning

• URL: http://arxiv.org/abs/2507.05707v1
• Date: Tue, 08 Jul 2025 06:35:16 GMT
• Title: Agentic-R1: Distilled Dual-Strategy Reasoning
• Authors: Weihua Du, Pranjal Aggarwal, Sean Welleck, Yiming Yang,
• Abstract summary: Current long chain-of-thought (long-CoT) models excel at mathematical reasoning but rely on slow and error-prone natural language traces.<n>We introduce a fine-tuning framework, DualDistill, that distills complementary reasoning strategies from multiple teachers into a unified student model.<n>Our method improves accuracy across a range of tasks, including both computation-intensive and standard benchmarks.
• Score: 44.848089301154026
• License: http://creativecommons.org/licenses/by/4.0/
• 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 across a range of tasks, including both computation-intensive and standard benchmarks, demonstrating the effectiveness of multi-strategy distillation in achieving robust and efficient reasoning. Our project is available at https://github.com/StigLidu/DualDistill

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