---
title: "xLM: A Python Package for Non-Autoregressive Language Models"
description: "The authors present xLM, a Python package built on PyTorch, PyTorch Lightning and Hydra. 22 references."
authors: ["Dhruvesh Patel", "Durga Prasad Maram", "Sai Sreenivas Chintha", "Benjamin Rozonoyer", "Andrew McCallum"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)"
source: "https://aclanthology.org/2026.eacl-demo.31/"
doi: "10.18653/v1/2026.eacl-demo.31"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/patel-2026-xlm-python-package-non-autoregressive/"
---
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# xLM: A Python Package for Non-Autoregressive Language Models

**Authors:** Dhruvesh Patel, Durga Prasad Maram, Sai Sreenivas Chintha, [Benjamin Rozonoyer](https://md.research.elamur.ai/free/en/authors/benjamin-rozonoyer/) and [Andrew McCallum](https://md.research.elamur.ai/free/en/authors/andrew-mccallum/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-demo.31/) · [PDF](https://aclanthology.org/2026.eacl-demo.31.pdf) · [DOI 10.18653/v1/2026.eacl-demo.31](https://doi.org/10.18653/v1/2026.eacl-demo.31) · [OpenAlex](https://openalex.org/W7140121580)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Research on non-autoregressive text generation is growing, including masked diffusion, Gaussian diffusion, insertion-based and edit-based language models. Unlike autoregressive LMs, these approaches lack common tooling. Most implementations are one-off, and each method needs its own collation, loss and decoding code, so fair comparisons are hard. The authors present xLM, a Python package built on PyTorch, PyTorch Lightning and Hydra. It aims to speed up prototyping of small non-autoregressive LMs.

Its design follows a "maximal independence" philosophy:
- Shared core components (Harness, DataModule) delegate model-specific work to pluggable parts.
- Each model lives in a self-contained folder that can be copied from a template.
- Hydra configs let users swap whole components without editing code.

A walkthrough shows how to build the Insertion Language Model (ILM) step by step. To check fidelity, the authors re-implement several models on star-graph planning tasks and on LM1B. Results stay close to earlier published figures. A companion package, xlm-models, is planned to provide small pre-trained models for the community.

## Key points
- **Core architecture:** a Harness (a LightningModule) holds the Model, LossFunction, Predictor and Metrics slots. A TextDataModule manages one DatasetManager per dataset, mapped many-to-many to the train/val/test/predict stages.
- **Training setups:** single-GPU, multi-GPU and multi-node, with both map-style and iterable datasets.
- **Workflows:** a single `xlm` command runs train, eval and generate jobs, plus a `debug=overfit` mode. Models are discovered through `XLM_MODELS_PACKAGES` or `XLM_MODELS_PATH`.
- **Star-graph planning benchmark (StarEasy and harder variants):** ARLM, MLM, MDLM and ILM results fall within 2% of the original papers. On the Hard variant, ILM reaches 97.5% sequence accuracy, while the other models score roughly 21–25%.
- **LM1B benchmark:** 12-layer ARLM, MDLM and ILM models are scored by negative log-likelihood under Llama 3.2, and the outcomes are close to those reported by Patel et al. (2025). Planned extensions include edit-based models and non-text tasks such as molecule generation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Massachusetts Amherst](https://md.research.elamur.ai/free/en/universities/university-of-massachusetts-amherst/) — University, Amherst Center, United States · 106 works · 66,692 citations

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## Cite

Cite as: Dhruvesh Patel, Durga Prasad Maram, Sai Sreenivas Chintha, Benjamin Rozonoyer and Andrew McCallum. 2026. xLM: A Python Package for Non-Autoregressive Language Models. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{patel2026xlm,
  title = {{xLM: A Python Package for Non-Autoregressive Language Models}},
  author = {Dhruvesh Patel, Durga Prasad Maram, Sai Sreenivas Chintha, Benjamin Rozonoyer and Andrew McCallum},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-demo.31},
  url = {https://doi.org/10.18653/v1/2026.eacl-demo.31}
}
```

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## References

22 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*