mlsanityData to 14 Oct 2026

Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models

arXiv 2601.12247 ↗First posted 18 Jan 2026cs.CL cs.AI cs.LGcited by 2

On the map: Diffusion models → Discrete and text diffusion

also in 1 more
Abstract (arXiv)

Diffusion Language Models (DLMs) present a promising non-sequential paradigm for text generation, distinct from standard autoregressive (AR) approaches. However, current decoding strategies often adopt a reactive stance, underutilizing the global bidirectional context to dictate global trajectories. To address this, we propose Plan-Verify-Fill (PVF), a training-free paradigm that grounds planning via quantitative validation. PVF actively constructs a hierarchical skeleton by prioritizing high-leverage semantic anchors and employs a verification protocol to operationalize pragmatic structural stopping where further deliberation yields diminishing returns. Extensive evaluations on LLaDA-8B-Instruct and Dream-7B-Instruct demonstrate that PVF reduces the Number of Function Evaluations (NFE) by up to 65% compared to confidence-based parallel decoding across benchmark datasets, unlocking superior efficiency without compromising accuracy.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Discrete and text diffusion, one of 14 directions in Diffusion models. Builds on 45 of its 54 references that are in the map. First posted on arXiv 18 Jan 2026.

Builds on

45 of 54 references.

Closest ancestors · 18

Same direction, newest first

  1. 2025ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding15 Dec
  2. 2025From Bits to Rounds: Parallel Decoding with Exploration for Diffusion Language Models26 Nov
  3. 2025KLASS: KL-Guided Fast Inference in Masked Diffusion Models7 Nov
  4. 2025Planned Diffusion20 Oct
  5. 2025Planner and Executor: Collaboration between Discrete Diffusion And Autoregressive Models in Reasoning17 Oct
  6. 2025Next Semantic Scale Prediction via Hierarchical Diffusion Language Models8 Oct
  7. 2025Dream 7B: Diffusion Large Language Models21 Aug
  8. 2025Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing8 Aug
  9. 2025Diffusion Beats Autoregressive in Data-Constrained Settings21 Jul
  10. 2025Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking30 May
  11. 2025Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel Decoding22 May
  12. 2025Large Language Diffusion Models14 Feb
6 more
  1. 2024Scaling Diffusion Language Models via Adaptation from Autoregressive Models23 Oct
  2. 2024Simple and Effective Masked Diffusion Language Models11 Jun
  3. 2024Simplified and Generalized Masked Diffusion for Discrete Data6 Jun
  4. 2024Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data6 Jun
  5. 2023Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution25 Oct
  6. 2021Autoregressive Diffusion Models5 Oct

Other references · 27

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  4. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  5. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,848
  6. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
  7. 2022Fast Inference from Transformers via Speculative Decodingcited by 1,653
  8. 2022MaskGIT: Masked Generative Image Transformercited by 1,099
  9. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,027
  10. 2023Accelerating Large Language Model Decoding with Speculative Samplingcited by 1,006
  11. 2024Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusioncited by 681
  12. 2025Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoningcited by 559
15 more
  1. 2025Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decodingcited by 420
  2. 2025LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Modelscited by 285
  3. 2025Reasoning with Exploration: An Entropy Perspectivecited by 281
  4. 2025Mercury: Ultra-Fast Language Models Based on Diffusioncited by 235
  5. 2025Fast-dLLM v2: Efficient Block-Diffusion LLMcited by 139
  6. 2025Thought Anchors: Which LLM Reasoning Steps Matter?cited by 129
  7. 2025dParallel: Learnable Parallel Decoding for dLLMscited by 65
  8. 2023Guiding Language Model Reasoning with Planning Tokenscited by 58
  9. 2025Wide-In, Narrow-Out: Revokable Decoding for Efficient and Effective DLLMscited by 46
  10. 2025Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimizationcited by 25
  11. 2025Diffusion LLM with Native Variable Generation Lengths: Let [EOS] Lead the Waycited by 22
  12. 2025Free Draft-and-Verification: Toward Lossless Parallel Decoding for Diffusion Large Language Modelscited by 19
  13. 2023Interpretable Math Word Problem Solution Generation Via Step-by-step Planningcited by 17
  14. 2025Thinking Inside the Mask: In-Place Prompting in Diffusion LLMscited by 13
  15. 2024Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuningcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Confidence-Based Decoding is Provably Efficient for Diffusion Language Modelscited by 18
  2. 2026Think First, Diffuse Fast: Improving Diffusion Language Model Reasoning via Autoregressive Plan Conditioningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026From Position Risks to Block Survival: Faster Generation for Diffusion Language Models
  2. 2026Locally Coherent Parallel Decoding in Diffusion Language Models
  3. 2026Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?
  4. 2025Beyond Confidence: Adaptive and Coherent Decoding for Diffusion Language Models
  5. 2026AsyncLane: Decoupling Refinement from Advancement in Diffusion Language Model Decoding
  6. 2026Rethinking the Generation Order of Block Diffusion Language Models
  7. 2026CD4LM: Consistency Distillation and aDaptive Decoding for Diffusion Language Models
  8. 2026Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation