68 lines
3.0 KiB
Markdown
68 lines
3.0 KiB
Markdown
---
|
||
tags:
|
||
- qwen2.5
|
||
- RL
|
||
- reasoning
|
||
library_name: transformers
|
||
pipeline_tag: text-generation
|
||
license: apache-2.0
|
||
base_model: voidful/Llama-3.2-8B-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.
|
||
|
||
[](https://arxiv.org/abs/2510.02227) [](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/Llama3.2-8B-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},
|
||
}
|
||
``` |