mlsanityData to 7 Oct 2026

Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow

arXiv 2601.15593 ↗First posted 22 Jan 2026cs.CL cs.AI cs.LGcited by 8

On the map: Diffusion models → Discrete and text diffusion

Abstract (arXiv)

Masked Diffusion Language Models (MDLMs) promise parallel token generation and arbitrary-order decoding, yet it remains unclear to what extent current models truly realize these capabilities. We characterize MDLM behavior along two dimensions -- parallelism strength and generation order -- using Average Finalization Parallelism (AFP) and Kendall's tau. We evaluate eight mainstream MDLMs (up to 100B parameters) on 58 benchmarks spanning knowledge, reasoning, and programming. The results show that MDLMs still lag behind comparably sized autoregressive models, mainly because parallel probabilistic modeling weakens inter-token dependencies. Meanwhile, MDLMs exhibit adaptive decoding behavior: their parallelism and generation order vary significantly with the task domain, the stage of reasoning, and whether the output is correct. On tasks that require "backward information" (e.g., Sudoku), MDLMs adopt a solution order that tends to fill easier Sudoku blanks first, highlighting their advantages. Finally, we provide theoretical motivation and design insights supporting a Generate-then-Edit paradigm, which mitigates dependency loss while retaining the efficiency of parallel decoding.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Discrete and text diffusion, one of 14 directions in Diffusion models. Builds on 50 of its 71 references that are in the map. First posted on arXiv 22 Jan 2026.

Builds on

50 of 71 references.

Closest ancestors · 10

Same direction, newest first

  1. 2025DiRL: An Efficient Post-Training Framework for Diffusion Language Models23 Dec
  2. 2025LLaDA2.0: Scaling Up Diffusion Language Models to 100B10 Dec
  3. 2025From Next-Token to Next-Block: A Principled Adaptation Path for Diffusion LLMs7 Dec
  4. 2025TiDAR: Think in Diffusion, Talk in Autoregression12 Nov
  5. 2025SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation7 Oct
  6. 2025Dream 7B: Diffusion Large Language Models21 Aug
  7. 2025A Survey on Diffusion Language Models14 Aug
  8. 2025DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation25 Jun
  9. 2025Theoretical Benefit and Limitation of Diffusion Language Model13 Feb
  10. 2024Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning18 Oct

Other references · 40

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  3. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  4. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  5. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,018
  6. 2021Program Synthesis with Large Language Modelscited by 3,509
  7. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  8. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,818
  9. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
  10. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
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  12. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,758
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  2. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  3. 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,359
  4. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
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  11. 2023CMMLU: Measuring massive multitask language understanding in Chinesecited by 436
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  13. 2022Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thoughtcited by 334
  14. 2024CRUXEval: A Benchmark for Code Reasoning, Understanding and Executioncited by 294
  15. 2023SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Modelscited by 246
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  22. 2023CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?cited by 89
  23. 2020OCNLI: Original Chinese Natural Language Inferencecited by 86
  24. 2024KOR-Bench: Benchmarking Language Models on Knowledge-Orthogonal Reasoning Taskscited by 67
  25. 2024Are Your LLMs Capable of Stable Reasoning?cited by 65
  26. 2025ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMscited by 42
  27. 2025HARDMath2: A Benchmark for Applied Mathematics Built by Students as Part of a Graduate Classcited by 5
  28. 2025Nexus: A Lightweight and Scalable Multi-Agent Framework for Complex Tasks Automationcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 8.

8 papers.

  1. 2026Swordsman: Entropy-Driven Adaptive Block Partition for Efficient Diffusion Language Modelscited by 3
  2. 2026Locally Coherent Parallel Decoding in Diffusion Language Modelscited by 2
  3. 2026Fast-dLLM++: Fr'echet Profile Decoding for Faster Diffusion LLM Inferencecited by 2
  4. 2026Neither Parallel Nor Sequential: How DiffusionGemma Actually Commits Tokenscited by 2
  5. 2026Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Modelscited by 1
  6. 2026Conditional Total Correlation and the Serial Depth of Adaptive Parallel Samplingcited by 1
  7. 2026PSD: Pushing the Pareto Frontier of Diffusion LLMs via Parallel Speculative Decodingcited by 0
  8. 2026Decoding in Order-Agnostic Language Models: Chain-Rule Deviation and Uniform Spreadingcited by 0

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  1. 2026Reliable Parallel Decoding in Masked Diffusion Language Models
  2. 2026Generation Order and Parallel Decoding in Masked Diffusion Models: An Information-Theoretic Perspective
  3. 2026Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?
  4. 2026Mean-Field Parallel Decoding for Discrete Diffusion Language Models
  5. 2025On Powerful Ways to Generate: Autoregression, Diffusion, and Beyond
  6. 2026Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models
  7. 2025On the Reasoning Abilities of Masked Diffusion Language Models
  8. 2026Rethinking the Generation Order of Block Diffusion Language Models