---
library_name: peft
pipeline_tag: text-generation
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
- Qwen/Qwen2.5-7B-Instruct
- meta-llama/Llama-2-7b-chat-hf
---

# DynamicPO: Dynamic Preference Optimization for Recommendation

This repository contains the model weights (LoRA adapters) for **DynamicPO**, a plug-and-play dynamic preference optimization framework for LLM-based recommender systems.

DynamicPO is designed to align Large Language Models (LLMs) with user preferences while mitigating "preference optimization collapse." This phenomenon occurs in multi-negative alignment when increasing the number of negative samples leads to performance degradation despite a decreasing training loss.


## Key Features

DynamicPO comprises two adaptive mechanisms:
- **Dynamic Boundary Negative Selection**: Identifies and prioritizes informative negatives near the model's decision boundary.
- **Dual-Margin Dynamic beta Adjustment**: Calibrates optimization strength per sample according to boundary ambiguity.

## Resources

- **Paper**: [DynamicPO: Dynamic Preference Optimization for Recommendation](https://huggingface.co/papers/2605.00327)
- **GitHub Repository**: [xingyuHuxingyu/DynamicPO](https://github.com/xingyuHuxingyu/DynamicPO)
- **Dataset**: [DynamicPO Dataset](https://huggingface.co/datasets/xingyuHuxingyu/DynamicPO-Data)

## Base Models
- meta-llama/Llama-2-7b-chat-hf
- meta-llama/Meta-Llama-3-8B-Instruct
- Qwen/Qwen2.5-7B-Instruct

## Citation

This work received DASFAA 2026 Best Paper Award. If you find this work useful, please consider citing:

```bibtex
@inproceedings{hu2026dynamicpo,
  title={DynamicPO: Dynamic Preference Optimization for Recommendation},
  author={Hu, Xingyu and Zhang, Kai and Wu, Jiancan and Wang, Shuli and Wang, Chi and Chen, Wenshuai and Zhu, Yinhua and Wang, Haitao and Wang, Xingxing and Wang, Xiang},
  booktitle={International Conference on Database Systems for Advanced Applications},
  pages={372--387},
  year={2026},
  organization={Springer}
}
```

## Acknowledgment

This implementation is built upon the [TRL library](https://github.com/huggingface/trl).