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TL;DR

Masked diffusion LMs (LLaDA, MDLM) need many denoising steps at inference → slow. Fast DLLM speeds this up with: (1) an optimal denoising schedule that allocates more steps where they matter, (2) importance sampling to skip low-value steps, and (3) DualCache for KV reuse. Result: 3-10x fewer steps with minimal quality loss.

Problem: Diffusion LMs are slow at inference

100+ denoising steps, each = full forward pass

Observation

Insight: Not all steps are equally important

Early steps (high noise) contribute much more to quality than late steps

Solution

Method: Three acceleration techniques

Optimal Schedule

Concentrate steps where L_t is large

Importance Sampling

Probabilistically skip low-value steps

DualCache

Reuse KV across denoising steps

Result: 3-10x speedup, near-lossless quality

10 steps ≈ 100 steps quality

Compatible with any masked diffusion LM

1. Background: The Speed Problem

In masked diffusion LMs, generation requires T denoising steps. At each step, the model does a full forward pass through all layers to predict masked tokens. With T=100, generating a sequence takes 100x more compute than a single AR forward pass.

\text{AR cost} = L \times C_{\text{forward}} \quad \text{vs} \quad \text{Diffusion cost} = T \times C_{\text{forward}}

2. Key Insight: Non-uniform Step Importance

The contribution of each denoising step to the overall ELBO is highly non-uniform:

L_t = -\mathbb{E}\left[\frac{1}{t \cdot L} \sum_{i: x_t^i = \texttt{[M]}} \log p_\theta(x_0^i \mid x_t)\right]

L_t

Loss at timestep t — measures how "hard" this denoising step is

t

Timestep (noise level). High t = many masks = hard. Low t = few masks = easy

x_t^i = \texttt{[M]}

Only masked positions contribute to the loss

Intuition: Early steps (high t) have high L_t because the model sees few tokens and must guess a lot. Late steps (low t) have low L_t because almost everything is revealed — just filling in 1-2 obvious tokens.

ELBO = -∑ Lₜ — hover over each bar to see the per-timestep loss

t=1

t=2

t=3

t=4

t=5

t=6

t=7

t=8

Total ELBO loss: 1.790

3. Optimal Denoising Schedule

Instead of uniform timesteps, Fast DLLM places steps proportionally to their importance using the inverse CDF:

F(t) = \frac{\int_0^t L_s \, ds}{\int_0^1 L_s \, ds}

t_k = F^{-1}\!\left(\frac{k}{K}\right), \quad k = 0, 1, \ldots, K

F(t)

Cumulative importance up to time t. F(0)=0, F(1)=1

L_s

Per-step loss at time s (from Section 2)

F^{-1}

Inverse CDF — finds the time t where cumulative importance = target

K

Total number of denoising steps we want to use (e.g. 10)

t_k

The k-th timestep in our schedule

Suppose L_t looks like: high at t=0.8-1.0, medium at t=0.4-0.8, low at t=0-0.4. With K=4 steps:

Uniform schedule

t = [1.0, 0.75, 0.50, 0.25, 0.0]
Equal spacing, wastes steps in low-L region

Optimal schedule

t = [1.0, 0.90, 0.78, 0.55, 0.0]
Dense in high-L region, sparse in low-L

Each step in the optimal schedule handles an equal share of the total importance. This means no compute is wasted on easy steps.

4. Importance Sampling for Step Skipping

Beyond scheduling, Fast DLLM can skip steps entirely. The idea: if a step contributes very little to quality, we can skip it with high probability:

P(\text{execute step } k) = \min\!\left(1,\; \frac{L_{t_k} \cdot K}{\sum_{j=1}^{K} L_{t_j}}\right)

P(\text{execute step } k)

Probability of actually running step k

L_{t_k}

Importance of step k (its ELBO contribution)

K

Total steps in the schedulemin(1, ...)Cap at 1 — important steps are always executed

Steps with above-average importance (L_tk > average) → probability = 1, always run. Steps with below-average importance → run with reduced probability. Reweighting keeps the estimate unbiased.

5. DualCache: KV Cache for Diffusion

Standard AR models use KV-cache to avoid recomputing key/value projections. But diffusion models change all token representations at each step — so KV-cache seems impossible. Fast DLLM's insight: tokens that are already unmasked DON'T change much between steps.

\text{KV}_{t} = \underbrace{\text{KV}_{\text{unmasked}}}_{\text{cached from step } t+1} \cup \underbrace{\text{KV}_{\text{masked}}}_{\text{recomputed at step } t}

6. Experiments & Results

Method	Steps	PPL	Speedup
MDLM (uniform)	100	31.2	1x
Uniform (fewer steps)	25	35.4	4x
Uniform (fewer steps)	10	45.8	10x
Fast DLLM (adaptive)	10	33.1	10x

Key result: With 10 steps (10x speedup), adaptive schedule: 33.1 PPL vs uniform's 45.8 PPL. Nearly matches the 100-step baseline (31.2). This makes diffusion LMs practical for real-time applications.

7. Limitations & Future Work

Schedule estimation: Need to estimate L_t on a validation set first — adds a pre-processing step
DualCache approximation: Reusing unmasked KV is an approximation that can degrade at very few steps
Distillation unexplored: Could potentially learn to generate in fewer steps via distillation (like in image diffusion)

8. Connections to Other Work

LLaDA/ MDLM

Fast DLLM is compatible with any masked diffusion LM — it's a sampling strategy, not a new model. Drop it into LLaDA or MDLM for instant speedup.

Block Diffusion

Addresses speed from a different angle (block-level AR). Both can be combined: block diffusion with Fast DLLM's adaptive schedule within each block.

DDIM / DPM-Solver(image diffusion)

Analogous works in continuous image diffusion that also optimize the denoising schedule. Fast DLLM adapts these ideas to the discrete (masked) setting.

9. Additional Resources

Fast dLLM (arXiv)Original paper MDLM (arXiv)The base model this accelerates