---
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}, 
}
```