metadata
license: mit
pipeline_tag: text-generation
ELF: Embedded Language Flows
ELF is a class of continuous diffusion language models based on continuous-time Flow Matching. Unlike existing diffusion language models (DLMs), ELF predominantly stays within the continuous embedding space until the final time step, where it maps to discrete tokens using a shared-weight network. This formulation makes it straightforward to adapt established techniques from image-domain diffusion models, such as classifier-free guidance (CFG).
- Paper: ELF: Embedded Language Flows
- Repository: GitHub
Evaluation and Inference
The official implementation is provided in JAX. To evaluate the pre-trained checkpoints, follow the installation instructions in the GitHub repository and use the following commands from the src/ directory:
Unconditional Generation (OpenWebText)
python eval.py \
--config configs/training_configs/train_owt_ELF-B.yml \
--checkpoint_path embedded-language-flows/ELF-B-owt
Conditional Generation (Summarization/Translation)
# XSum (summarization)
python eval.py \
--config configs/training_configs/train_xsum_ELF-B.yml \
--checkpoint_path embedded-language-flows/ELF-B-xsum
# WMT14 De-En (translation)
python eval.py \
--config configs/training_configs/train_de-en_ELF-B.yml \
--checkpoint_path embedded-language-flows/ELF-B-de-en
Citation
@article{elf2026,
title={ELF: Embedded Language Flows},
author={Hu, Keya and Qiu, Linlu and Lu, Yiyang and Zhao, Hanhong and Li, Tianhong and Kim, Yoon and Andreas, Jacob and He, Kaiming},
journal={arXiv preprint arXiv:2605.10938},
year={2026}
}