---
library_name: transformers
license: mit
datasets:
- hendrydong/preference_700K
base_model:
- RLHFlow/LLaMA3-SFT-v2
pipeline_tag: text-classification
---


# rlhflow-llama-3-sft-segment Model Card

- **Paper:** [Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model
](https://arxiv.org/abs/2501.02790)

- **Model:** [yyqoni/rlhflow-llama-3-sft-8b-v2-segment-rm-700k](https://huggingface.co/yyqoni/rlhflow-llama-3-sft-8b-v2-segment-rm-700k)

## Method


The segment reward model assigns rewards to semantically meaningful text segments, segmented dynamically with an entropy-based threshold. It is trained on binary preference labels from human feedback, optimizing a Bradley-Terry loss function that aggregates segment rewards using the average function.

## Architecture
<div align=center>

![image/png](https://cdn-uploads.huggingface.co/production/uploads/605e8dfd5abeb13e714c4c18/xeGwtrpnx2bWFg5ZOHA7R.png)

</div>


## Training

The phi-instruct-segment model is fine-tuned from **RLHFlow/LLaMA3-SFT-v2** on the **hendrydong/preference_700K dataset**.



## Citation

If you find this model or our research useful, please consider citing our paper:

```bibtex
@misc{yin2025segmentingtextlearningrewards,
      title={Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model}, 
      author={Yueqin Yin and Shentao Yang and Yujia Xie and Ziyi Yang and Yuting Sun and Hany Awadalla and Weizhu Chen and Mingyuan Zhou},
      year={2025},
      eprint={2501.02790},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.02790},
}
```