# VILA-Lab/Awesome-DLMs

The official GitHub repo for the survey paper "A Survey on Diffusion Language Models".

Repository: https://github.com/VILA-Lab/Awesome-DLMs
Canonical: https://ross.abutalabs.com/products/awesome-dlms
Homepage: https://arxiv.org/abs/2508.10875
License Family: other
Last push: 2026-08-31T05:43:24+00:00

## Health v2 (maintenance only)
Score: 63/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 100, release rhythm 35, longevity 29
- inputs: {"age_days": 415, "days_push": 2, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1202, forks 60 (observed 2026-09-01T02:13:58.091670+00:00)

## What it is
A curated awesome-list accompanying the survey paper 'A Survey on Diffusion Language Models', collecting papers, code, demos, benchmarks, and frameworks for diffusion-based language models. It organizes resources across discrete, continuous, and multimodal DLMs, plus training and inference techniques.

## Use cases
- find papers on diffusion language models
- learn about non-autoregressive text generation
- track state-of-the-art DLM research and demos
- find training and inference frameworks for diffusion LMs
- compare discrete vs continuous diffusion language models
- find benchmarks for parallel text generation

## When to choose
- you are researching diffusion-based language models and want a comprehensive, updated reading list
- you want demos and playgrounds for models like LLaDA, Mercury, or Dream
- you need a taxonomy of DLM training and inference techniques

## When to avoid
- you need runnable software rather than a curated paper list
- you only care about standard autoregressive LLM tooling

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, nlp, machine-learning
- domain: large-language-models, artificial-intelligence, awesome-lists, tutorials
- platform: -
- tags: awesome-list, diffusion-language-models, survey-paper, research-collection, reading-list, natural-language-processing, web-server

## Member repositories
- VILA-Lab/Awesome-DLMs (main) score 63

## Provenance
- Observed fields: from GitHub, fetched 2026-09-01T02:13:58.091670+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:22:12.941087+00:00, confidence not recorded.
  - readme: https://github.com/VILA-Lab/Awesome-DLMs (fetched 2026-09-01T02:13:58.091670+00:00, sha 4b8d1082cf2c)
  - homepage: https://arxiv.org/abs/2508.10875 (fetched 2026-08-29T12:29:55.798242+00:00, sha b614baa9184a)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T12:29:55.802292+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T12:29:55.806202+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T12:29:55.808018+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T12:29:55.804439+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.