PRISM: Principled Reasoning for Integrated Safety in Multimodality

This repository provides the implementation of PRISM, an alignment framework that integrates principled reasoning with safety through structured, multi-step reasoning.

🚀 Quick Start

Prerequisites

conda create -n PRISM python=3.10
conda activate PRISM
pip install 'ms-swift[all]' -U
pip install vllm

1) Model Training

📦 Datasets

We open-source the training datasets on Hugging Face:

• PRISM-CoT: https://huggingface.co/datasets/andyc03/PRISM-CoT
• PRISM-DPO: https://huggingface.co/datasets/andyc03/PRISM-DPO

First, prepare the data. We have released the PRISM-CoT and PRISM-DPO datasets. Convert your dataset to a Swift-compatible format by providing the absolute path to your data folder:

python utils/formatting.py --folder /your_path_here/PRISM_COT

Then add the special tokens for your model using utils/add_tokens.py:

python utils/add_tokens.py --model_path /your_mode_path_here

Now you can train your PRISM model. Update the JSON and model path in training_scripts/qwen2_vl.sh, for example:

cd training_scripts

# For Qwen2-VL with full-parameters SFT
bash qwen2_vl.sh

📦 Model Weights

We provide the model weights used in our experiments on Hugging Face:

• Qwen2-VL-PRISM-SFT: https://huggingface.co/andyc03/Qwen2-VL-PRISM-SFT
• Qwen2-VL-PRISM-DPO: https://huggingface.co/andyc03/Qwen2-VL-PRISM-DPO

2) MCTS Data Generation

If you want to generate preference data using Monte Carlo Tree Search (MCTS), we provide scripts to help you do so:

cd PRISM_DPO_data

First, change the model path of your downloaded PRISM-CoT model in scripts/activate_vllm.sh, then launch it:

bash scripts/activate_vllm.sh

Next, configure your model path and data in config/qwen_tree_generate.yaml, then run MCTS data generation:

# Then run MCTS data generation
bash scripts/generate_MCT.sh

Configuration parameters:

• actor_model_dir: Path to your model
• train_prompt_path: Input prompts for data generation
• iterations: Number of MCTS iterations (default: 200)
• c: UCB exploration parameter (default: 1.5)
• max_depth: Maximum reasoning depth (default: 5)

3) Test-Time Scaling

Please refer to TTS/TTS.md for running details.

📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

📚 Citation

If you use PRISM in your research, please consider citing our paper:

@misc{li2025prismrobustvlmalignment,
      title={PRISM: Robust VLM Alignment with Principled Reasoning for Integrated Safety in Multimodality}, 
      author={Nanxi Li and Zhengyue Zhao and Chaowei Xiao},
      year={2025},
      eprint={2508.18649},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2508.18649}, 
}

🙏 Acknowledgments

Built on top of excellent open-source projects including ms-swift, vLLM, and STAIR.

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For questions, issues, or discussions, please open an issue in this repository or contact the author at [email protected].