---
tags:
- qwen2.5
- RL
- reasoning
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
---

# Introduction

**AMPO**, a novel framework that intelligently leverages guidance from multiple, diverse teacher models, intervening only when the on-policy model fails. Our two core contributions, Adaptive Multi-Guidance Replacement and Comprehension-based Guidance Selection, ensure that this external knowledge is used both efficiently and effectively.

[![Paper](https://img.shields.io/badge/paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2510.02227) [![Github](https://img.shields.io/badge/AMPO-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white)](https://github.com/SII-Enigma/AMPO)

### Key Highlights:
- **Adaptive Multi-Guidance Replacement**: Minimizes intervention by providing external guidance only upon complete on-policy failure, preserving self-discovery while enhancing reasoning efficiency.
- **Comprehension-based Guidance Selection**: Improves learning effectiveness by guiding the model to assimilate the most comprehensible external solutions, demonstrably boosting performance.
- **Superior Performance:** Achieves better performance and efficiency compared to using RL or SFT alone.


### Multi-Guidance Pool

Teacher Models: AceReason-Nemotron-1.1-7B, DeepSeek-R1-Distill-Qwen-7B, OpenR1-Qwen-7B, Qwen3-8B(thinking)

## Inference Example

Here’s an example of using AMPO for inference:

```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

model_path = "SII-Enigma/Qwen2.5-7B-Ins-AMPO"

question = "which number is larger? 9.11 or 9.9?"

tokenizer = AutoTokenizer.from_pretrained(model_path)
messages = [{"role": "user", "content": question}]
chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

llm = LLM(model=model_path)
params = SamplingParams(temperature=0.6, max_tokens=8192)
outputs = llm.generate([chat], params)
print(outputs[0].outputs[0].text)
```

# Acknowledgement

AMPO builds upon [LUFFY](https://github.com/ElliottYan/LUFFY), [veRL](https://github.com/volcengine/verl), [RLPR](https://github.com/OpenBMB/RLPR) and utilizes [vLLM](https://github.com/vllm-project/vllm) for inference. We utilize [Math-Verify](https://github.com/huggingface/Math-Verify) for math reasoning evaluation. We thank the open-source community for codes, datasets and backbones.



# Citation
If you find our model, data, or evaluation code useful, please kindly cite our paper:
```bib
@misc{yuan2025teacheradaptivemultiguidancepolicy,
      title={More Than One Teacher: Adaptive Multi-Guidance Policy Optimization for Diverse Exploration}, 
      author={Xiaoyang Yuan and Yujuan Ding and Yi Bin and Wenqi Shao and Jinyu Cai and Jingkuan Song and Yang Yang and Heng Tao Shen},
      year={2025},
      eprint={2510.02227},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.02227}, 
}
```