mlsanityData to 14 Oct 2026

EPIC: Efficient and Parallel Inference under CFG Constraints for Diffusion Language Models

arXiv 2606.00722 ↗First posted 30 May 2026cs.CL cs.AIcited by 1

On the map: Diffusion models → Discrete and text diffusion

Abstract (arXiv)

Controlling language model outputs is essential for ensuring structural validity, reliability, and downstream usability, and diffusion language models are no exception. Recent advances in diffusion language model decoding have extended output control beyond regular constraints to context-free grammar (CFG) constraints. However, the prior CFG-constrained decoder can be up to four times slower than unconstrained decoding, forcing a trade-off between the correctness benefits of CFG constraints and the decoding efficiency of diffusion models. A key source of this overhead is sequential validity checking, which limits parallel token commitment and adds repeated validation costs. We propose EPIC, a CFG-constrained decoding framework designed to improve this efficiency-correctness trade-off. In order to reduce decoding overhead without sacrificing syntactic correctness, EPIC combines lexing memoization, relaxed compatible subset selection for parallel commit, and validation using Earley-style parsing instead of deterministic automata. This design enables multiple compatible tokens to be committed together while avoiding repeated lexing and expensive validation. Experiments on three benchmarks using four models show that EPIC improves the efficiency-correctness trade-off, bringing runtime close to unconstrained levels while maintaining comparable syntactic and functional correctness. Relative to the prior CFG-constrained decoder, EPIC achieves a best-case inference-time reduction of 67.2%. Our implementation is available at https://github.com/hyundong98/EPIC-Decoding .

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Discrete and text diffusion, one of 14 directions in Diffusion models. Builds on 21 of its 34 references that are in the map. First posted on arXiv 30 May 2026.

Builds on

21 of 34 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Dream-Coder 7B: An Open Diffusion Language Model for Code1 Sep
  2. 2025Dream 7B: Diffusion Large Language Models21 Aug
  3. 2025DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation25 Jun
  4. 2025Large Language Diffusion Models14 Feb
  5. 2024Simple and Effective Masked Diffusion Language Models11 Jun
  6. 2023Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution25 Oct

Other references · 15

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
  3. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 411
  4. 2023CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-Xcited by 300
  5. 2023Efficient Guided Generation for Large Language Modelscited by 200
  6. 2023Grammar-Constrained Decoding for Structured NLP Tasks without Finetuningcited by 197
  7. 2024Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generationcited by 101
  8. 2024SynCode: LLM Generation with Grammar Augmentationcited by 69
  9. 2024Automata-based constraints for language model decodingcited by 51
  10. 2025Flexible and Efficient Grammar-Constrained Decodingcited by 43
  11. 2025ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMscited by 43
  12. 2025DINGO: Constrained Inference for Diffusion LLMscited by 16
3 more
  1. 2025Constrained Decoding of Diffusion LLMs with Context-Free Grammarscited by 14
  2. 2026DAPD: Dependency-Aware Parallel Decoding via Attention for Diffusion LLMscited by 14
  3. 2026Lookahead-then-Verify: Reliable Constrained Decoding for Diffusion LLMs under Context-Free Grammarscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Constraints Are Graphs, Not Chains: Exact Decoding for Diffusion Language Modelscited by 0

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  1. 2026Constrained Decoding for Diffusion Language Models via Efficient Inference over Finite Automata
  2. 2025WeDLM: Reconciling Diffusion Language Models with Standard Causal Attention for Fast Inference
  3. 2025Beyond Confidence: Adaptive and Coherent Decoding for Diffusion Language Models
  4. 2025Finish First, Perfect Later: Test-Time Token-Level Cross-Validation for Diffusion Large Language Models
  5. 2026SimSD: Simple Speculative Decoding in Diffusion Language Models
  6. 2024Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion
  7. 2025Decoding Large Language Diffusion Models with Foreseeing Movement
  8. 2026Accelerating Masked Diffusion Large Language Models: A Survey of Efficient Inference Techniques