Update README.md (#3)

- Update README.md (6366fbb3878044cef7d85ab679ed7680086745cd)


Co-authored-by: WuShaoyu <Saoyu@users.noreply.huggingface.co>

README.md

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 license: apache-2.0
+pipeline_tag: text-generation
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-Our D-CORE paper: arxiv.org/abs/2602.02160 
+
+# D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
+
+This repository contains the weights for **D-CORE** (**D**ecomposing tasks and **Co**mposing **Re**asoning 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](https://huggingface.co/papers/2602.02160)
+- **Arxiv**: [2602.02160](https://arxiv.org/abs/2602.02160)
+- **Code**: [EfficientAI (GitHub)](https://github.com/alibaba/EfficientAI)
+
+## 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
-| Model | bool | enum | number | list | object_short | object_deep | atom | single_turn_singal_function | single_turn_parallel_function | single_turn | multiple_turn_switch | multiple_turn_adjust | multiple_turn | similar_api | profile | normal_summary | incomplete | error | irrelevant | special_summary | agent_multi_turn | agent_multi_turn_process | agent_multi_step | agent_multi_step_process | agent_summary | Summary |
-|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|
-|**D-CORE-8B**| 0.9 | 0.98 | 0.98 | 0.98 | 0.36 | 0.76 | 0.827 | 0.85 | 0.7 | 0.775 | 0.64 | 0.6 | 0.62 | 0.78 | 0.82 | 0.779 | 0.58 | 0.82 | 0.96 | 0.787 | 0.433 | 0.668 | 0.75 | 0.808 | 0.592 | 0.752 |
+<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:
+```bibtex
+@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}
+}
+```