mlsanityData to 7 Oct 2026

Commitment Before Realization: When Classifier-Free Guidance Becomes Unnecessary in Masked Diffusion Language Models

arXiv 2608.08082 ↗First posted 8 Aug 2026cs.CLcited by 0

On the map: Diffusion models → Discrete and text diffusion

Abstract (arXiv)

Classifier-free guidance (CFG) is usually kept on throughout masked diffusion language model decoding, although its benefit varies across prompts and over time. We study when CFG is actually needed by comparing, from any partial output, the probability of eventual constraint satisfaction under continued CFG and under base-only continuation. Their difference defines the remaining value of guidance. Guidance dependence is highly prompt-specific. Many prompts already succeed without CFG, while for others it provides no measurable benefit or can be harmful. For prompts that do benefit, the gain is often concentrated early. We define the commitment horizon astar as the earliest point from which switching all remaining decoding to the base model reduces final success by no more than a chosen tolerance. Under the base model, the corresponding success probability, or committor, is a martingale. To first order, CFG's per-step effect is governed by the covariance between the guidance logit direction and the successor committor. This gives a local account of when guidance can help, but it does not by itself locate the horizon. Among prompts with an observed preterminal horizon, astar is usually early and varies more within constraint families than between them. Freezing each prompt at its own cross-fitted horizon is noninferior to full CFG on all 13 subtasks at the prespecified margin, even while many tokens remain masked. This separates commitment from realization. The boundary also identifies a later region in which higher parallelism adds only a small cost in constraint success, although fluency still degrades with parallel width. For failed trajectories, reopening committed positions improves recovery in both failure modes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Discrete and text diffusion, one of 14 directions in Diffusion models. Builds on 30 of its 41 references that are in the map. First posted on arXiv 8 Aug 2026.

Builds on

30 of 41 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Simple and Effective Masked Diffusion Language Models11 Jun
  2. 2024Simplified and Generalized Masked Diffusion for Discrete Data6 Jun
  3. 2023Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution25 Oct

Other references · 27

  1. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  2. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  3. 2021Diffusion Schr"odinger Bridge with Applications to Score-Based Generative Modelingcited by 594
  4. 2024Guiding a Diffusion Model with a Bad Version of Itselfcited by 286
  5. 2024Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Modelscited by 240
  6. 2021Path Integral Sampler: a stochastic control approach for samplingcited by 175
  7. 2022Improving Sample Quality of Diffusion Models Using Self-Attention Guidancecited by 151
  8. 2024Simple Guidance Mechanisms for Discrete Diffusion Modelscited by 148
  9. 2024CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Modelscited by 138
  10. 2023CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Samplingcited by 124
  11. 2024Unlocking Guidance for Discrete State-Space Diffusion and Flow Modelscited by 124
  12. 2024Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Modelscited by 107
15 more
  1. 2024A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Datacited by 88
  2. 2023Spontaneous Symmetry Breaking in Generative Diffusion Modelscited by 74
  3. 2023COLLIE: Systematic Construction of Constrained Text Generation Taskscited by 69
  4. 2024Analysis of Classifier-Free Guidance Weight Schedulerscited by 64
  5. 2025dParallel: Learnable Parallel Decoding for dLLMscited by 59
  6. 2023Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Latercited by 52
  7. 2025A Fourier Space Perspective on Diffusion Modelscited by 48
  8. 2024DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised h-transformcited by 44
  9. 2023Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scalecited by 26
  10. 2023A framework for conditional diffusion modelling with applications in motif scaffolding for protein designcited by 22
  11. 2025Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Maskingcited by 21
  12. 2026Conditional Diffusion Guidance under Hard Constraint: A Stochastic Analysis Approachcited by 10
  13. 2025What Exactly Does Guidance Do in Masked Discrete Diffusion Modelscited by 9
  14. 2026The Entropic Signature of Class Speciation in Diffusion Modelscited by 7
  15. 2025Estimating Committor Functions via Deep Adaptive Sampling on Rare Transition Pathscited by 3

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  2. 2026Guidance Is Not a Hyperparameter: Learning Dynamic Control in Diffusion Language Models
  3. 2026A Theoretical Analysis of Why Masked Diffusion Models Mitigate the Reversal Curse
  4. 2026Commit Locally, Exit Globally: Coordinating Adaptive Sampling and Early Exit in Diffusion Language Models
  5. 2026Neither Parallel Nor Sequential: How DiffusionGemma Actually Commits Tokens
  6. 2026C2FG: Control Classifier-Free Guidance via Score Discrepancy Analysis
  7. 2026Unmask the State: When Does State Adaptation Matter for Masked Diffusion Language Models
  8. 2026When to Commit? Towards Variable-Size Self-Contained Blocks for Discrete Diffusion Language Models