Update README.md

README.md

@@ -1,3 +1,46 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- LoRID-Math/MATH
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- mistralai/Mistral-7B-v0.1
+pipeline_tag: text-generation
+library_name: peft
+tags:
+- math
+- reasoning
+---
+
+# LoRID: A Reasoning Distillation Method via Multi-LoRA Interaction
+
+📃 [Paper](https://arxiv.org/abs/2508.13037) • 💻 [Code](https://github.com/Xinhe-Li/LoRID) • 🤗 [HF Repo](https://huggingface.co/LoRID-Math)
+
+## Abstract
+
+The models for "[Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction](https://arxiv.org/abs/2508.13037)" [IJCAI 2025].
+
+## Key Contributions
+
+- We focus on the mathematical reasoning distillation task and propose a novel method **LoRID**, which draws inspiration from the human beings teaching and learning pattern.
+- We introduce knowledge during data augmentation and propose multi-LoRA interaction during model distillation, which improves the student’s reasoning abilities.
+- Experimental results show that with the interaction between System 1 and System 2, **LoRID** outperforms previous state-of-the-art approaches and can be easily and effectively integrated into any Chain-of-Thought distillation method.
+
+## Citation
+
+If this work is helpful, please kindly cite as:
+
+```bibtex
+@misc{li2025largemodelsteachstudent,
+      title={Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction}, 
+      author={Xinhe Li and Jiajun Liu and Peng Wang},
+      year={2025},
+      eprint={2508.13037},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2508.13037}, 
+}
+```