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

arXiv: https://arxiv.org/abs/2606.00722 · HTML: https://mlsanity.com/paper/2606.00722/

## Key facts

- Cited by 1 papers 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.

## Placed in

- Diffusion models / [Discrete and text diffusion](https://mlsanity.com/diffusion/discrete-and-text-diffusion/)

## Builds on (most cited first, 21 of 21)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [Large Language Diffusion Models](https://mlsanity.com/paper/2502.09992/) (2025)
- [Simple and Effective Masked Diffusion Language Models](https://mlsanity.com/paper/2406.07524/) (2024)
- [Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution](https://mlsanity.com/paper/2310.16834/) (2023)
- [Dream 7B: Diffusion Large Language Models](https://mlsanity.com/paper/2508.15487/) (2025)
- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X](https://mlsanity.com/paper/2303.17568/) (2023)
- [Efficient Guided Generation for Large Language Models](https://mlsanity.com/paper/2307.09702/) (2023)
- [Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning](https://mlsanity.com/paper/2305.13971/) (2023)
- [DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation](https://mlsanity.com/paper/2506.20639/) (2025)
- [Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation](https://mlsanity.com/paper/2403.06988/) (2024)
- [Dream-Coder 7B: An Open Diffusion Language Model for Code](https://mlsanity.com/paper/2509.01142/) (2025)
- [SynCode: LLM Generation with Grammar Augmentation](https://mlsanity.com/paper/2403.01632/) (2024)
- [Automata-based constraints for language model decoding](https://mlsanity.com/paper/2407.08103/) (2024)
- [Flexible and Efficient Grammar-Constrained Decoding](https://mlsanity.com/paper/2502.05111/) (2025)
- [ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMs](https://mlsanity.com/paper/2510.04767/) (2025)
- [DINGO: Constrained Inference for Diffusion LLMs](https://mlsanity.com/paper/2505.23061/) (2025)
- [Constrained Decoding of Diffusion LLMs with Context-Free Grammars](https://mlsanity.com/paper/2508.10111/) (2025)
- [DAPD: Dependency-Aware Parallel Decoding via Attention for Diffusion LLMs](https://mlsanity.com/paper/2603.12996/) (2026)
- [Lookahead-then-Verify: Reliable Constrained Decoding for Diffusion LLMs under Context-Free Grammars](https://mlsanity.com/paper/2602.00612/) (2026)

## Built on by (most cited first, 1 of 1)

- [Constraints Are Graphs, Not Chains: Exact Decoding for Diffusion Language Models](https://mlsanity.com/paper/2609.32900/) (2026)

## 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 .