mlsanityData to 7 Oct 2026

Planned Diffusion

arXiv 2510.18087 ↗First posted 20 Oct 2025cs.AIcited by 8

On the map: Diffusion models → Discrete and text diffusion

Abstract (arXiv)

Most large language models are autoregressive: they generate tokens one at a time. Discrete diffusion language models can generate multiple tokens in parallel, but sampling from them requires a denoising order: a strategy for deciding which tokens to decode at each step. Determining a good denoising order is difficult, and existing approaches use heuristics that create a steep trade-off between quality and latency. We propose planned diffusion, a system that trains the model to determine its own denoising order. Planned diffusion uses a single model that transitions between autoregressive and diffusion-based generation: first, the model autoregressively generates a plan that partitions the response into semantically independent chunks; second, the model denoises all chunks in parallel. The autoregressive plan enables the model to define the denoising order itself. On AlpacaEval, planned diffusion achieves 1.27x to 1.81x speedup over autoregressive generation with only 0.87% to 5.4% drop in win rate, establishing a new Pareto frontier for parallel generation with discrete diffusion. Additionally, planned diffusion's instruction following quality continues to improve with more finetuning compute, while the autoregressive baseline plateaus. Our implementation provides simple runtime knobs that offer tunable control over the quality-latency trade-off.

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 46 of its 63 references that are in the map. First posted on arXiv 20 Oct 2025.

Builds on

46 of 63 references.

Closest ancestors · 14

Same direction, newest first

  1. 2025Diffusion Language Models Know the Answer Before Decoding27 Aug
  2. 2025Dream 7B: Diffusion Large Language Models21 Aug
  3. 2025Diffusion Beats Autoregressive in Data-Constrained Settings21 Jul
  4. 2025FlashDLM: Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion27 May
  5. 2025dKV-Cache: The Cache for Diffusion Language Models21 May
  6. 2025Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models12 Mar
  7. 2025Large Language Diffusion Models14 Feb
  8. 2025Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions10 Feb
  9. 2024Think While You Generate: Discrete Diffusion with Planned Denoising8 Oct
  10. 2024Discrete Copula Diffusion2 Oct
  11. 2024Simple and Effective Masked Diffusion Language Models11 Jun
  12. 2024Simplified and Generalized Masked Diffusion for Discrete Data6 Jun
2 more
  1. 2023Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution25 Oct
  2. 2021Autoregressive Diffusion Models5 Oct

Other references · 32

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  6. 2024Qwen2.5 Technical Reportcited by 4,713
  7. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  8. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  9. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  10. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  11. 2022Fast Inference from Transformers via Speculative Decodingcited by 1,615
  12. 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,359
20 more
  1. 2023Accelerating Large Language Model Decoding with Speculative Samplingcited by 975
  2. 2024Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluatorscited by 865
  3. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 712
  4. 2022Efficiently Scaling Transformer Inferencecited by 529
  5. 2025Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decodingcited by 404
  6. 2025HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Modelcited by 176
  7. 2025dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Cachingcited by 168
  8. 2019Insertion Transformer: Flexible Sequence Generation via Insertion Operationscited by 123
  9. 2025Accelerating Diffusion LLMs via Adaptive Parallel Decodingcited by 108
  10. 2023Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generationcited by 103
  11. 2025Learning Adaptive Parallel Reasoning with Language Modelscited by 58
  12. 2024MonoFormer: One Transformer for Both Diffusion and Autoregressioncited by 45
  13. 2025Wide-In, Narrow-Out: Revokable Decoding for Efficient and Effective DLLMscited by 45
  14. 2025ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Computecited by 36
  15. 2025Esoteric Language Models: A Family of Any-Order Diffusion LLMscited by 33
  16. 2025Hogwild! Inference: Parallel LLM Generation via Concurrent Attentioncited by 32
  17. 2025Multiverse: Your Language Models Secretly Decide How to Parallelize and Merge Generationcited by 30
  18. 2025Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decodingcited by 26
  19. 2024APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decodingcited by 23
  20. 2025Enabling Autoregressive Models to Fill In Masked Tokenscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 8.

8 papers.

  1. 2026Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Modelcited by 12
  2. 2026dLLM: Simple Diffusion Language Modelingcited by 12
  3. 2026Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Modelscited by 2
  4. 2026Think First, Diffuse Fast: Improving Diffusion Language Model Reasoning via Autoregressive Plan Conditioningcited by 1
  5. 2026TRIMS: Trajectory-Ranked Instruction Masked Supervision for Diffusion Language Modelscited by 1
  6. 2026DPRM: A Plug-in Doob h transform-induced Token-Ordering Module for Discrete Diffusion Modelscited by 1
  7. 2026Continuous Interaction Diffusion: A Diffusion-Native Runtime for Asynchronous Tool-Augmented Reasoningcited by 0
  8. 2026d-OPD: Future-Aware On-Policy Distillation for Block Diffusion Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Models
  2. 2026Path-Dependent Denoising: A Non-Conservative Field Perspective on Order Collapse in Diffusion Language Models
  3. 2025A Survey on Diffusion Language Models
  4. 2025Breaking AR's Sampling Bottleneck: Provable Acceleration via Diffusion Language Models
  5. 2026Diffusion Language Models: An Experimental Analysis
  6. 2026Denoising Surface: Modeling and Predicting Inference Cost for Diffusion LLM Serving
  7. 2026Optimizers for Diffusion Models: A Controlled Benchmark
  8. 2026Hierarchical Continuous Diffusion Language Models