D-CORE-8B

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

This repository contains the weights for D-CORE (Decomposing tasks and Composing Reasoning processes), a two-stage training framework designed to enhance the task decomposition and reflective reasoning capabilities of Large Reasoning Models (LRMs) for complex tool use.

Introduction

Effective tool use and reasoning are essential capabilities for large reasoning models (LRMs) to address complex real-world problems. Through empirical analysis, the authors identified that current LRMs lack the capability of sub-task decomposition in complex tool use scenarios, leading to "Lazy Reasoning."

To address this, D-CORE proposes a two-stage training framework:

1. Self-distillation: Incentivizes the LRM's task decomposition reasoning capability.
2. Diversity-aware Reinforcement Learning (RL): Restores the LRM's reflective reasoning capability.

D-CORE achieves robust tool-use improvements across diverse benchmarks and model scales. Notably, D-CORE-14B establishes a new state-of-the-art on BFCLv3, outperforming 70B models despite being 5$\times$ smaller.

Resources

• Paper: D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
• Arxiv: 2602.02160
• Code: EfficientAI (GitHub)

Authors

Bowen Xu, Shaoyu Wu, Hao Jiang, Kai Liu, Xin Chen, Lulu Hu, Bin Yang

Performance

BFCL

In our network environment, for the Web Search No Snippet task, we are unable to access certain websites (e.g., Wikipedia), which results in some deviation in the No Snippet scores.

<table> <thead> <tr> <th rowspan="4">Model</th> <th rowspan="4">Overall</th> <th colspan="7" style="text-align:center">Agentic</th> <th colspan="5" style="text-align:center">Multi Turn</th> <th colspan="10" style="text-align:center">Single Turn</th> <th colspan="2" style="text-align:center">Hallucination Measurement</th> <th colspan="2" style="text-align:center">Format Sensitivity</th> </tr> <tr> <th colspan="3" style="text-align:center">Web Search</th> <th colspan="4" style="text-align:center">Memory</th> <th rowspan="3">Overall Acc</th> <th rowspan="3">Base</th> <th rowspan="3">Miss Func</th> <th rowspan="3">Miss Param</th> <th rowspan="3">Long Context</th> <th colspan="5" style="text-align:center">Non-live</th> <th colspan="5" style="text-align:center">Live</th> <th rowspan="3">Relevance</th> <th rowspan="3">Irrelevance</th> <th rowspan="3">Max Delta</th> <th rowspan="3">SD</th> </tr> <tr> <th rowspan="2">Summary</th> <th rowspan="2">Base</th> <th rowspan="2">No Snippet</th> <th rowspan="2">Summary</th> <th rowspan="2">KV</th> <th rowspan="2">Vector</th> <th rowspan="2">Recusive Sum</th> <th rowspan="2">Overall Acc</th> <th rowspan="2">Simple</th> <th rowspan="2">Multiple</th> <th rowspan="2">Parallel</th> <th rowspan="2">Multiple Parallel</th> <th rowspan="2">Overall Acc</th> <th rowspan="2">Simple</th> <th rowspan="2">Multiple</th> <th rowspan="2">Parallel</th> <th rowspan="2">Multiple Parallel</th> </tr> <tr> </tr> </thead> <tbody> <tr> <td style="white-space: nowrap"><strong>D-CORE-8B</strong></td> <td style="text-align: center">53.15</td> <td style="text-align: center">23.00</td> <td style="text-align: center">36.00</td> <td style="text-align: center">10.00</td> <td style="text-align: center">19.14</td> <td style="text-align: center">9.03</td> <td style="text-align: center">16.77</td> <td style="text-align: center">31.61</td> <td style="text-align: center">64.88</td> <td style="text-align: center">75.50</td> <td style="text-align: center">65.00</td> <td style="text-align: center">60.50</td> <td style="text-align: center">58.50</td> <td style="text-align: center">86.85</td> <td style="text-align: center">75.92</td> <td style="text-align: center">92.50</td> <td style="text-align: center">92.00</td> <td style="text-align: center">87.00</td> <td style="text-align: center">75.80</td> <td style="text-align: center">78.29</td> <td style="text-align: center">75.02</td> <td style="text-align: center">100.00</td> <td style="text-align: center">66.67</td> <td style="text-align: center">75.00</td> <td style="text-align: center">89.99</td> <td style="text-align: center">75.0</td> <td style="text-align: center">24.67</td> </tr> </tbody> </table>

Tau-Bench & Tau2-Bench

We use Qwen3-235B-A22B-Instruct-2507 as the user model. For each task, we sample 5 times and take the average as the final result.

<table> <thead> <tr> <th rowspan="3">Model</th> <th colspan="3" style="text-align:center">Tau-Bench</th> <th colspan="4" style="text-align:center">Tau2-Bench</th> </tr> <tr> <th rowspan="2">Overall</th> <th rowspan="2">Retail</th> <th rowspan="2">Airline</th> <th rowspan="2">Overall</th> <th rowspan="2">Retail</th> <th rowspan="2">Airline</th> <th rowspan="2">Telecom</th> </tr> <tr> </tr> </thead> <tbody> <tr> <td style="white-space: nowrap"><strong>D-CORE-8B</strong></td> <td style="text-align: center">44.9</td> <td style="text-align: center">53.0</td> <td style="text-align: center">36.8</td> <td style="text-align: center">35.8</td> <td style="text-align: center">43.2</td> <td style="text-align: center">37.1</td> <td style="text-align: center">27.2</td> </tr> </tbody> </table>

ACEBench

<table> <thead> <tr> <th rowspan="3">Model</th> <th rowspan="3">Overall</th> <th colspan="7" style="text-align:center">Atom</th> <th colspan="3" style="text-align:center">Single Turn</th> <th colspan="3" style="text-align:center">Multi Turn</th> <th rowspan="3" style="text-align:center">Similar API</th> <th rowspan="3" style="text-align:center">Preference</th> <th rowspan="3" style="text-align:center">Summary</th> <th colspan="4" style="text-align:center">Special</th> <th colspan="5" style="text-align:center">Agent</th> </tr> <tr> <th rowspan="2">Summary</th> <th rowspan="2">Bool</th> <th rowspan="2">Enum</th> <th rowspan="2">Number</th> <th rowspan="2">List</th> <th rowspan="2">Object Short</th> <th rowspan="2">Object Deep</th> <th rowspan="2">Summary</th> <th rowspan="2">Singal Function</th> <th rowspan="2">Parallel Function</th> <th rowspan="2">Summary</th> <th rowspan="2">Switch</th> <th rowspan="2">Adjust</th> <th rowspan="2">Summary</th> <th rowspan="2">Incomplete</th> <th rowspan="2">Error</th> <th rowspan="2">Irrelevant</th> <th rowspan="2">Summary</th> <th rowspan="2">Multi Turn</th> <th rowspan="2">Multi Turn Process</th> <th rowspan="2">Multi Step</th> <th rowspan="2">Multi Step Process</th> </tr> <tr> </tr> </thead> <tbody> <tr> <td style="white-space: nowrap"><strong>D-CORE-8B</strong></td> <td style="text-align: center">75.2</td> <td style="text-align: center">82.7</td> <td style="text-align: center">90.0</td> <td style="text-align: center">98.0</td> <td style="text-align: center">98.0</td> <td style="text-align: center">98.0</td> <td style="text-align: center">36.0</td> <td style="text-align: center">76.0</td> <td style="text-align: center">77.5</td> <td style="text-align: center">85.0</td> <td style="text-align: center">70.0</td> <td style="text-align: center">62.0</td> <td style="text-align: center">64.0</td> <td style="text-align: center">60.0</td> <td style="text-align: center">78.0</td> <td style="text-align: center">82.0</td> <td style="text-align: center">77.9</td> <td style="text-align: center">78.7</td> <td style="text-align: center">58.0</td> <td style="text-align: center">82.0</td> <td style="text-align: center">96.0</td> <td style="text-align: center">59.2</td> <td style="text-align: center">43.3</td> <td style="text-align: center">66.8</td> <td style="text-align: center">75.0</td> <td style="text-align: center">80.8</td> </tr> </tbody> </table>

Citation

If you find our work useful, please cite:

@article{xu2026dcore,
  title={D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use},
  author={Xu, Bowen and Wu, Shaoyu and Jiang, Hao and Liu, Kai and Chen, Xin and Hu, Lulu and Yang, Bin},
  journal={arXiv preprint arXiv:2602.02160},
  year={2026}
}