LLaDA 1.5 EU AI Act Compliance Profile

LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models

We introduce LLaDA 1.5, a competitive large diffusion language model, trained by variance-reduced preference optimization (VRPO), as presented in the paper LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models.

Compared with LLaDA-8B-Instruct, LLaDA 1.5 achieves better performance on a wide range of tasks, including Math, Code, and Alignment tasks.

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Code

Inference

The LLaDA 1.5 model is available on Huggingface. Please employ the transformers to load.

from transformers import AutoModel, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained('GSAI-ML/LLaDA-1.5', trust_remote_code=True)
model = AutoModel.from_pretrained('GSAI-ML/LLaDA-1.5', trust_remote_code=True, torch_dtype=torch.bfloat16)

The model is based on LLaDA-8B-Instruct, you can use the code for LLaDA-8B-Instruct to inference.

Citation

Please consider cite:

@article{zhu2025llada,
  title={LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models},
  author={Zhu, Fengqi and Wang, Rongzhen and Nie, Shen and Zhang, Xiaolu and Wu, Chunwei and Hu, Jun and Zhou, Jun and Chen, Jianfei and Lin, Yankai and Wen, Ji-Rong and others},
  journal={arXiv preprint arXiv:2505.19223},
  year={2025}
}

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