Update README.md

README.md

@@ -12,4 +12,45 @@ pipeline_tag: text-generation
 base_model: Qwen/Qwen3-4B
 datasets:
 - divelab/opdlm_train_data
+arxiv: 2606.06712
 ---
+# 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](https://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](https://huggingface.co/Qwen/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](https://huggingface.co/datasets/divelab/opdlm_train_data)
+
+## Results
+For detailed results and benchmarks, please refer to our paper: [arxiv.org/abs/2606.06712](https://arxiv.org/abs/2606.06712)
+
+## Citation
+```bibtex
+@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},
+}
+```