OPDLM
Collection
Data and checkpoints for Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation • 10 items • Updated • 2
OPDLM-8B
OPDLM-8B 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.
Highlights
- Converted, not pretrained from scratch: built from a strong ARLM, reusing its prior.
- Training-efficient: ~0.066B 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: Qwen3-8B
- Model type: Block diffusion language model (decoder-based)
- Block size: 4
- Parameters: ~8B
- Language: English
- License: MIT
Training
- Method: On-policy distillation from a frozen ARLM teacher into a block DLM student.
- Conversion budget: ~0.066B tokens
- Data: opdlm_train_data
Evaluation
| Benchmark | Score |
|---|---|
| MMLU | 70.9 |
| MMLU-Pro | 53.7 |
| GPQA-Diamond | 36.1 |
| IFEval | 50.1 |
| GSM8K | 87.1 |
| MATH500 | 71.2 |
| AIME-24 | 14.7 |
| AIME-25 | 12.4 |
| HumanEval | 59.8 |
| MBPP | 48.7 |
Decoding: static (one token per step)
Citation
[FILL: BibTeX once the paper/arXiv is up]
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