YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Model Card for Model ID

Model Details

Model Description

Developed by: Hao Peng@THUGKEG
Model type: RL trained LLMs
Language(s) (NLP): English, Chinese
License: apache-2.0
Finetuned from model [optional]: allenai/Llama-3.1-Tulu-3-8B-SFT

Model Sources [optional]

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

Training Details

The model is trained using RL with VerIF, using train data VerInstruct.

VerIF is a practical and efficient method for verification in instruction-following reinforcement learning. Built on the idea of Reinforcement Learning with Verifiable Rewards (RLVR), VerIF integrates rule-based code checks with LLM-based reasoning verification (e.g., QwQ-32B) to provide accurate and scalable reward signals.

The model is optimized for instruction-following, without affecting other general capabilities.

Evaluation Results

We evaluate the model on several representative instruction-following benchmarks, including IFEval, Multi-IF, SysBench, FollowBench, and etc..

You can find more details in our github repo (https://github.com/THU-KEG/VerIF). If you find this model helpful, 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}, 
}