---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
---

# Hybrid Policy Distillation: Qwen2.5-1.5B Student

This repository contains a **Qwen2.5-1.5B student model** distilled from **Qwen2.5-7B-Thinking** using **Hybrid Policy Distillation (HPD)**, as presented in the paper [Hybrid Policy Distillation for LLMs](https://huggingface.co/papers/2604.20244).

## Overview

Knowledge distillation (KD) is a powerful paradigm for compressing large language models (LLMs). Hybrid Policy Distillation (HPD) is a framework designed to make policy distillation more stable and efficient for reasoning-oriented models. It integrates the complementary advantages of forward and reverse KL to balance mode coverage and mode-seeking, and combines off-policy data with lightweight, approximate on-policy sampling.

- **Paper:** [Hybrid Policy Distillation for LLMs](https://huggingface.co/papers/2604.20244)
- **Repository:** [zwhong714/Hybrid-Policy-Distillation](https://github.com/zwhong714/Hybrid-Policy-Distillation)

## Benchmark Performance

The following table shows the performance of the distilled student model compared to the teacher model across various reasoning benchmarks:

| Model | AIME24 | AIME25 | AMC | MATH | OlympiadMath | GPQA |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| Qwen2.5-7B-Thinking (Teacher) | 28.13 | 27.19 | 71.72 | 87.48 | 58.50 | 43.43 |
| **Qwen2.5-1.5B-Thinking (Student)** | **7.71** | **9.89** | **39.84** | **63.40** | **32.53** | **28.09** |

## Citation

If you find this model or the HPD framework useful in your research, please cite the following work:

```bibtex
@article{hong2024hybrid,
  title={Hybrid Policy Distillation for LLMs},
  author={Hong, Zhang-Wei and others},
  journal={arXiv preprint arXiv:2604.20244},
  year={2024}
}
```