---
license: cc-by-4.0
---

# 💡 Reward Modeling from Natural Language Human Feedback

<p align="left">
  <a href="https://arxiv.org/abs/2601.07349">
    <img
      src="https://img.shields.io/badge/arXiv-RM--NLHF-red?logo=arxiv" style="display: inline-block; vertical-align: middle;"
      alt="RM-NLHF Paper on arXiv"
    />
  </a>
  <a href="https://github.com/Tongyi-ConvAI/Qwen-Character/tree/main/Character-GenRM-NLHF" target="_blank" style="margin: 2px;">
      <img 
        alt="Github" src="https://img.shields.io/badge/Github -RM--NLHF--Codebase-536af5?color=536af5&logo=github" style="display: inline-block; vertical-align: middle;"
        alt="RM-NLHF Codebase"
      />
  </a>
    <a href="https://huggingface.co/Tongyi-ConvAI/RM-NLHF-Qwen-7B" target="_blank" style="margin: 2px;">
      <img 
        alt="HF Model: RM-NLHF-7B" src="https://img.shields.io/badge/%F0%9F%A4%97%20_Model-RM--NLHF--7B-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"
        alt="HF Model: RM-NLHF-7B"
      />
  </a>
    <a href="https://huggingface.co/Tongyi-ConvAI/RM-NLHF-Qwen-7B" target="_blank" style="margin: 2px;">
      <img 
        alt="HF Model: RM-NLHF-32B" src="https://img.shields.io/badge/%F0%9F%A4%97%20_Model-RM--NLHF--32B-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"
        alt="HF Model: RM-NLHF-32B"
      />
  </a>
</p>

This is the official dataset used in paper "Reward Modeling from Natural Language Human Feedback".

# 🔑 Key Features

- RM-NLHF integrates multiple preference datasets.
- We employ Qwen3-235B-A22B-2507 to extract key points from the human-annotated commentary portion of HelpSteer3 and reformat them into structured bullet-point lists.
- In this repository, we are open-sourcing only the HelpSteer3 and Tulu-3-Pref-Personas-Instruction-Following portions of the data.


# 🧷 Citation

```
@misc{wang2026rewardmodelingnaturallanguage,
  title={Reward Modeling from Natural Language Human Feedback}, 
  author={Zongqi Wang and Rui Wang and Yuchuan Wu and Yiyao Yu and Pinyi Zhang and Shaoning Sun and Yujiu Yang and Yongbin Li},
  year={2026},
  eprint={2601.07349},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2601.07349}, 
}
```