# Adaptive Multi-Step Lookahead Decoding for Diffusion Language Models

arXiv: https://arxiv.org/abs/2607.15655 · HTML: https://mlsanity.com/paper/2607.15655/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Discrete and text diffusion, one of 14 directions in Diffusion models.
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Placed in

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

## Builds on (most cited first, 11 of 11)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Simplified and Generalized Masked Diffusion for Discrete Data](https://mlsanity.com/paper/2406.04329/) (2024)
- [Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding](https://mlsanity.com/paper/2505.22618/) (2025)
- [Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data](https://mlsanity.com/paper/2406.03736/) (2024)
- [dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching](https://mlsanity.com/paper/2506.06295/) (2025)
- [LLaDA2.0: Scaling Up Diffusion Language Models to 100B](https://mlsanity.com/paper/2512.15745/) (2025)

## Abstract (arXiv)

Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternative to autoregressive decoding. Recent search-based decoding methods improve the accuracy--efficiency trade-off by exploring multiple candidate decoding trajectories before committing token updates. However, existing search-based decoding methods face two key limitations. First, they offer limited guidance on when search is actually beneficial for the current decoding state. Second, they typically rely on a shallow one-step search horizon, which may favor locally promising but suboptimal decoding trajectories. To address these limitations, we propose PRED, a planner-guided recursive exploration framework for DLM decoding. PRED employs a learned planner to determine when search should be triggered, and performs adaptive multi-step rollout to evaluate candidate trajectories over longer horizons. Moreover, the same planner is recursively applied to intermediate rollout states to determine whether additional branch expansion is needed, allowing PRED to re-initiate search when necessary. Experiments across multiple benchmarks and DLM backbones show that PRED achieves a better accuracy--efficiency trade-off than existing confidence-based and search-based decoding methods.