metadata

license: apache-2.0
language:
  - en
  - zh
base_model:
  - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
pipeline_tag: text2text-generation
tags:
  - Reward_Model
  - Reasoning_Model

Model Card for Model ID

Model Details

Model Description

Developed by: Hao Peng@THUKEG
Model type: Generative reward model
Language(s) (NLP): English, CHinese
License: apache-2.0
Finetuned from model [optional]: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Model Sources [optional]

Repository: https://github.com/THU-KEG/VerIF
Paper: https://arxiv.org/abs/2506.09942

Training Details

Training Data

This model is trained from DeepSeek-R1-Distill-Qwen-7B using 131k critic data IF-Verifier-Data. This model is used for verifying soft constraints of instruction following. Deploying IF-Verifier-7B requires only one single H800 GPU, with an average reward computation time of 120 seconds per batch, which can be further reduced with multi-GPUs.

Results

The model trained using this model is comparable with that of QwQ 32B.

Summary

Please refer to our paper and our GitHub repo (https://github.com/THU-KEG/VerIF) for more details.

Citation

If this model helps, please kindly cite us:

@misc{peng2025verif,
      title={VerIF: Verification Engineering for Reinforcement Learning in Instruction Following}, 
      author={Hao Peng and Yunjia Qi and Xiaozhi Wang and Bin Xu and Lei Hou and Juanzi Li},
      year={2025},
      eprint={2506.09942},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.09942}, 
}