D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use

• URL: http://arxiv.org/abs/2602.02160v1
• Date: Mon, 02 Feb 2026 14:36:15 GMT
• Title: D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
• Authors: Bowen Xu, Shaoyu Wu, Hao Jiang, Kai Liu, Xin Chen, Lulu Hu, Bin Yang,
• Abstract summary: Large reasoning models (LRMs) lack the capability of sub-task decomposition in complex tool use scenarios, leading to Lazy Reasoning.<n>We propose a two-stage training framework that incentivizes LRMs' task decomposition reasoning capability via self-distillation and diversity-aware reinforcement learning.<n>D-CORE achieves robust tool-use improvements across diverse benchmarks and model scales.
• Score: 17.99381644283042
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective tool use and reasoning are essential capabilities for large reasoning models~(LRMs) to address complex real-world problems. Through empirical analysis, we identify that current LRMs lack the capability of sub-task decomposition in complex tool use scenarios, leading to Lazy Reasoning. To address this, we propose a two-stage training framework D-CORE~(\underline{\textbf{D}}ecomposing tasks and \underline{\textbf{Co}}mposing \underline{\textbf{Re}}asoning processes) that first incentivize the LRMs' task decomposition reasoning capability via self-distillation, followed by diversity-aware reinforcement learning~(RL) to restore LRMs' reflective reasoning capability. D-CORE achieves robust tool-use improvements across diverse benchmarks and model scales. Experiments on BFCLv3 demonstrate superiority of our method: D-CORE-8B reaches 77.7\% accuracy, surpassing the best-performing 8B model by 5.7\%. Meanwhile, D-CORE-14B establishes a new state-of-the-art at 79.3\%, outperforming 70B models despite being 5$\times$ smaller. The source code is available at https://github.com/alibaba/EfficientAI.

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