PRISM is an efficient inference framework designed for Discrete Diffusion Language Models (dLLMs), focusing on a favorable performance-efficiency trade-off by matching Best-of-N performance with substantially fewer Function Evaluations (NFE).

PRISM/
├── Dream/                   # Experiments for Dream
│   ├── Dream_Baseline/      # Standard baseline sampling (N=1)
│   └── Dream_Prism/         # Prism implementation
├── LLaDA/                   # Experiments for LLaDA 8B Instruct
│   ├── LLaDA_Baseline/      # Standard baseline sampling (N=1)
│   ├── LLaDA_Prism/         # PRISM implementation
│   └── LLaDA_Truthfulqa/    # TruthfulQA evaluation
└── LLaDA2mini/              # Experiments for LLaDA 2.0-mini
    ├── LLaDA2mini_Baseline/ # Standard baseline sampling (N=1)
    └── LLaDA2mini_Prism/    # Prism implementation

cd PRISM

For Dream Project:

cd Dream/Dream_Prism/eval_instruct
pip install -e .

For LLaDA_Truthfulqa:

cd LLaDA/LLaDA_Truthfulqa/lm-evaluation-harness
pip install -e .

For LLaDA and LLaDA2 Projects:

cd LLaDA/LLaDA_Prism
pip install -r requirements.txt

Evaluate Dream

cd Dream/Dream_Prism
bash scripts/run_gsm8k.sh
bash scripts/run_humaneval.sh
bash scripts/run_math500.sh
bash scripts/run_mbpp.sh

Evaluate LLaDA 8B Instruct

cd LLaDA/LLaDA_Prism
bash scripts/run_gsm8k.sh
bash scripts/run_humaneval.sh
bash scripts/run_math500.sh
bash scripts/run_mbpp.sh

Evaluate LLaDA 8B Instruct(Truthfulqa)

cd LLaDA/LLaDA_Truthfulqa
bash scripts/llada_prism.sh

Evaluate LLaDA 2.0-mini

cd LLaDA2mini/LLaDA2mini_Prism
bash scripts/run_gsm8k.sh
bash scripts/run_humaneval.sh
bash scripts/run_math500.sh
bash scripts/run_mbpp.sh

Each project folder contains a metrics/ directory used for calculating final accuracy and efficiency metrics. Usage Example:

python PRISM/LLaDA/LLaDA_Prism/metrics/gsm8k_all.py

This project is built upon preordinary/LLaDA2, ML-GSAI/LLaDA, DreamLM/Dream and EleutherAI/lm-evaluation-harness. Special thanks to the authors for their contributions.

If you find this work helpful, please consider citing:

@article{bai2026prism,
  title={Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models},
  author={Bai, Jinbin and Li, Yixuan and Zhu, Yuchen and Xin, Yi and Shi, Qingyu and Feng, Aosong and Liu, Xiaohong and Tao, Molei and Xue, Jianru and Li, Xiangtai and Yang, Ming-Hsuan},
  journal={arXiv preprint arXiv:2602.01842},
  year={2026}
}