---
title: "Beyond Hard Masks — paper by Zhong et al. (2026)"
description: "Masked diffusion language models (MDLMs) generate text in parallel by gradually unmasking positions. 47 references, cited by 2 papers here."
authors: ["Linhao Zhong", "Linyu Wu", "Bozhen Fang", "Tianjian Feng", "Chenchen Jing", "Wen Wang", "Jiaheng Zhang", "Hao Chen", "Chunhua Shen"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.315/"
doi: "10.18653/v1/2026.acl-long.315"
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/zhong-2026-beyond-hard-masks-progressive-token/"
---
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# Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models

**Authors:** [Linhao Zhong](https://md.research.elamur.ai/free/en/authors/linhao-zhong/), [Linyu Wu](https://md.research.elamur.ai/free/en/authors/linyu-wu/), Bozhen Fang, Tianjian Feng, [Chenchen Jing](https://md.research.elamur.ai/free/en/authors/chenchen-jing/), [Wen Wang](https://md.research.elamur.ai/free/en/authors/wen-wang/), [Jiaheng Zhang](https://md.research.elamur.ai/free/en/authors/jiaheng-zhang/), [Hao Chen](https://md.research.elamur.ai/free/en/authors/hao-chen/) and [Chunhua Shen](https://md.research.elamur.ai/free/en/authors/chunhua-shen/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/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.acl-long.315/) · [PDF](https://aclanthology.org/2026.acl-long.315.pdf) · [DOI 10.18653/v1/2026.acl-long.315](https://doi.org/10.18653/v1/2026.acl-long.315) · [OpenAlex](https://openalex.org/W7166804788)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [2](https://md.research.elamur.ai/free/en/papers/zhong-2026-beyond-hard-masks-progressive-token/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Masked diffusion language models (MDLMs) generate text in parallel by gradually unmasking positions. They have two weaknesses. First, once a token is committed, it can never be revised. Second, the model predicts distributions for every position at each step but keeps only a few, so most of that work is thrown away.

EvoToken-DLM addresses both by representing undecided tokens as soft probability mixtures over the vocabulary. Each token passes through four stages: mask, mask-blended soft token, pure soft token, and finally a discrete token. Soft embeddings are built from the top-K predicted candidates, and a ratio α controls how much of the mask embedding is mixed in.

Training uses continuous trajectory supervision. The model unrolls several consecutive refinement steps and receives a loss at each one, so training mirrors how inference actually works. The approach needs no architectural changes and can be adapted from existing MDLMs with light fine-tuning. It works with KV-caching and blockwise diffusion.

Across several backbones and benchmarks, it consistently beats binary-masking baselines with negligible added latency. This makes revisable decoding a practical, general upgrade for diffusion LMs.

## Key points
- The motivating example uses a 30-token prompt and 512 generated tokens. Only 2 of 542 predicted distributions are used per step; the other 540 are discarded.
- Token states: [MASK] → Soft([MASK] ∪ V) → Soft(V) → [Decode]. The final token is the highest-confidence prediction seen after the token entered the Soft(V) stage.
- The method was evaluated on three backbones, with results for LLaDA-1.5 in the appendix:
  - LLaDA-Instruct-8B;
  - Dream-Instruct-7B;
  - the blockwise model D2F-LLaDA.
- Benchmarks mentioned include Countdown and SVAMP. Performance is robust across a wide range of top-K values.
- Limitation: adapting backbones initialized from autoregressive models is harder and converges more slowly. Code is available at github.com/aim-uofa/EvoTokenDLM.

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

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## Cite

Cite as: Linhao Zhong, Linyu Wu, Bozhen Fang, Tianjian Feng, Chenchen Jing, Wen Wang, Jiaheng Zhang, Hao Chen and Chunhua Shen. 2026. Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{zhong2026beyond,
  title = {{Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models}},
  author = {Linhao Zhong, Linyu Wu, Bozhen Fang, Tianjian Feng, Chenchen Jing, Wen Wang, Jiaheng Zhang, Hao Chen and Chunhua Shen},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.315},
  url = {https://doi.org/10.18653/v1/2026.acl-long.315}
}
```

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*Updated 2 November 2026*