OPDLM-4B

OPDLM-4B is a block diffusion language model (DLM) obtained by post-training an autoregressive language model (ARLM) into a diffusion language model via on-policy distillation. arXiv report: arxiv.org/abs/2606.06712

Highlights

Converted, not pretrained from scratch: built from a strong ARLM, reusing its prior.
Training-efficient: ~0.076B tokens of conversion vs. ~50B tokens for from-scratch DLM training (same base ARLM).
Inference-efficient: parallel token decoding via block diffusion.

Model Details

Developed by: DIVE Lab, Texas A&M University
Base model: Qwen3-4B
Model type: Block diffusion language model (decoder-based)
Block size: 4
Parameters: ~4B
Language: English
License: MIT

Training

Method: On-policy distillation from a frozen ARLM teacher into a block DLM student.
Conversion budget: ~0.076B tokens
Data: opdlm_train_data

Results

For detailed results and benchmarks, please refer to our paper: arxiv.org/abs/2606.06712

Citation

@misc{su2026dataefficientautoregressivetodiffusionlanguagemodels,
      title={Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation}, 
      author={Xingyu Su and Jacob Helwig and Shubham Parashar and Atharv Chagi and Lakshmi Jotsna and Degui Zhi and James Caverlee and Dileep Kalathil and Shuiwang Ji},
      year={2026},
      eprint={2606.06712},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.06712},
}