D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
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~(\textbf{D}ecomposing tasks and \textbf{Co}mposing \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 5times smaller. The source code is available at https://github.com/alibaba/EfficientAI.
698172e2ce18b18628096033
Bowen Xu
claimed_verified
2026-02-05T10:56:55.418Z
6418142857c3a491a4d64f1f
/avatars/85db855cdc65ef9e3fd20d15e50ef1cc.svg
bowen xu
bowiehsu
user
bowiehsu
698172e2ce18b18628096034
Shaoyu Wu
698172e2ce18b18628096035
Hao Jiang
698172e2ce18b18628096036
Kai Liu
698172e2ce18b18628096037
Xin Chen
698172e2ce18b18628096038
Lulu Hu
698172e2ce18b18628096039
Bin Yang
D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
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~(\textbf{D}ecomposing tasks and \textbf{Co}mposing \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 5times smaller. The source code is available at https://github.com/alibaba/EfficientAI.
A two-stage training framework called D-CORE is proposed to improve large reasoning models' ability to decompose complex tasks and compose reasoning processes, achieving superior performance in tool-use benchmarks.
large reasoning models sub-task decomposition Lazy Reasoning self-distillation diversity-aware reinforcement learning BFCLv3