Accelerating Diffusion Large Language Models with SlowFast Sampling: The Three Golden Principles
Authors: Qingyan Wei
Yaojie Zhang
Zhiyuan Liu
Puyu Zeng
Yuxuan Wang
Biqing Qi
Dongrui Liu
Linfeng Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Large Language Models
Diffusion Models
Model Acceleration
Adaptive Sampling
Primary area: foundation or frontier models, including LLMs
TL;DR: SlowFast Sampling alternates between a slow exploratory phase and a fast parallel decoding phase, boosting diffusion LLMs by up to 34.22× with minimal quality loss.

Abstract: Diffusion-based language models (dLLMs) have emerged as a promising alternative to traditional autoregressive LLMs by enabling parallel token generation and significantly reducing inference latency. However, existing sampling strategies for dLLMs, such as confidence-based or semi-autoregressive decoding, often suffer from static behavior, leading to suboptimal efficiency and limited flexibility. In this paper, we propose SlowFast Sampling, a novel dynamic sampling strategy that adaptively alternates between exploratory and accelerated decoding stages. Our method is guided by three golden principles: certainty principle, convergence principle, and positional principle, which govern when and where tokens can be confidently and efficiently decoded. We further integrate our strategy with dLLM-Cache to reduce redundant computation. Extensive experiments across benchmarks and models show that SlowFast Sampling achieves up to 15.63× speedup on LLaDA with minimal accuracy drop, and up to 34.22× when combined with caching. Notably, our approach outperforms strong autoregressive baselines like LLaMA3 8B in throughput, demonstrating that well-designed sampling can unlock the full potential of dLLMs for fast and high-quality generation.

Published as a conference paper at ICLR 2026
ACCELERATING DIFFUSION LARGE LANGUAGE MOD- ELS WITH SLOWFAST SAMPLING: THE THREE GOLDEN PRINCIPLES
Qingyan Wei
Yaojie Zhang Biqing Qi
Zhiyuan Liu'
Puyu Zeng*
Yuxuan Wang'
Dongrui Liu?
Linfeng Zhang If
EPIC Lab, Shanghai Jiao Tong University
Shanghai Artificial Intelligence Laboratory
Code:
ABSTRACT
Diffusion-based language models (dLLMs) have emerged as a promising alternative to traditional autoregressive LLMs by enabling parallel token generation and significantly reducing inference latency, However, existing sampling strategies for dLLMs, such as confidence-basee or aemi-autoregressivy decoding, often suffer from static behavior, leading to suboptimal efficiency and limited flexibility. In this paper, propose SlowFast Sampling, novel dynamic sampling strategy that adaptively alternates between exploratory and accelerated decoding stages. Our method is guided by three golden principles: certainty principle, convergence principle, and positional principle, which govern when and where tokens can be confidently and efficiently decoded. We further integrate strategy with dILLM-Cache to reduce redundant computation. Extensive experiments across various benchmarks demonstrate the efficiency of our method. Specifically, the GPQA benchmark, SlowFast Sampling achieves up to 15.63x speedup on LLaDA with minimal accuracy drop, and up 34.22x when combined with caching. Notably, our approach outperforms strong autoregressive baselines like LLaMA3 8B in throughput, demonstrating that well-designed sampling can unlock the full potential of dL.LMs for fast and high-quality generation.
INTRODUCTION
Large Language Models (LLLMs) (Zhao al., 2025) have rapidly become cornerstone technologies in artificial intelligence, demonstrating remarkable capabilities across a diverse range of natural language understanding and generation tasks. However, the prevalent autoregressive nature of most LLMs, where tokens are generated sequentially one after another, introduces significant inference latency, particularly for long sequences. To address this inherent bottleneck, siffusion-based LLMs (dLLMs) (Ye et al. 2025 Nie et al. 2025b) have emerged models are capable of generating multiple tokens in parallel, departing from the strict token-by-token process. This parallel decoding capability offers the distinct advantage of potentially accelerating text generation significantly, positioning dI.LMM as a compelling and forward-looking direction for efficient language model inference.
promising alternative paradigm. These
However, current ways of sampling with dLLMs often don't perform as well as they could. Common methods include confidence-based selection (Chang al. (2022) like Fast-dLLM (Wu al. 2025). where tokens exceeding a confidence threshold are selected for decoding. Another popular method, semi-aut autorerrsssvvv decoding (Arriola et al., (025). divides the sequence into fixed blocks and decodes within them. Unfortunately. these methods frequently yield unsatisfactory results (i.e., significant accuracy drop when decoding many tokens parallel), and are characterized by a static, constant sampling speed throughout the generation process. This lack of flexibility highlights the need for a more dynamic sampling approach: one that can smartly decide how many tokens to sample at each step and where these tokens should be located in the sequence.
indicates the corresponding author.

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Published as a conference paper at ICLR 2026
255
Condиeps asaneee
Token #1 Token
Confidence
6.7
Confidence
alpp
Gonverges average ls 0:39
261
235
Tokan 615
Toker
Rbp
155 255
254
203
252
351
199
244
208
356
255
254
353
250 251 350 200 208
Decoding Step
Decoding Step
decodad token
(a) Token Confidence Converges during Decoding
(b) Token Confidence during Decoding
Figure 1: The Three Golden Principles for sampling in diffusion LLMs. (a) Convergence Principle: As decoding proceeds, the confidence values tokens largely converge to high values, while few tokens converge lower values. (b) The confidence map over 256 diffusion steps: High- confidence tokens (in deep red) emerge progressively and are preferentially decoded (the Certainty Principle), while selection tends to cluster in contiguous regions (the Positional Principle), enabling cache reuse and acceleration.
Motivated by these limitations, we introduce novel dynamic sampling approach designed to accelerate dLLMs, aiming to unlock the real potential of dLLMs under high-paralle, decoding. As illustrated in Figure [ our method is guided by three core observations, which we formulate as the Three Golden Principles for effective acceleration:
The Certainty Principle:
Tokens exhibiting higher confidence are inherently more determined. Consequently, they are more likely to be decoded correctly early in the process and require less adjustment subsequent diffusion steps.
The Convergence Principle: As the diffusion process unfolds and tokens are progressively refined, the semantic meaning of many tokens stabilizes, and their associated confidence scores converge towards steady value. This convergence indicates that these tokens have largely settled into their final form and require minimal further refinement.
The Positional Principle: We observe that even without explicit constraints, the model's sampling preferences often gravitate towards tokens in specific, frequently neighboring or clustered, positions. This inherent positional bias can be strategically exploited. For instance, parts of the sequence can be effectively cached, leading significant acceleration gains.
Integrating these principles, we propose SlowFast Sampling with phases: Exploratory Stage and an Accelerated Decoding Stage. In the exploratory stage, the model loosely decodes to locate spans with emerging certainty and convergence. The accelerated stage then parallelly decodes these high-certainty tokens, reducing effort on already determined parts. This division yields significant speedups, reaching 15.63x LLaDA and up 34.22x when combined with dLLM-Cache (Liu et al. 2025) on the GPQA benchmark, with minimal accuracy loss. Our contributions are threefold:
We propose three golden principles based on token certainty, convergence, and positional influence. which critically govern effective and efficient sampling in dLLMs.
Building on these principles, introduce SlowFast Sampling, novel two-stage dynamic strategy specifically designed to leverage these principles for optimal acceleration of dLLM.
Through experiments on various benchmarks, we demonstrate that SlowFast Sampling achieves significant inference acceleration (e.g., up to 15.63 x on LLaDA with SlowFast Sampling alone, and up to 34.22 when combined with DEL.MM-Cache on the GPQA dataset) without compromising response quality, thereby offering a superior speed-qualit trade-off compared to baseline and simpler sampling methods.
RELATED WORK
2.1 DIFFUSION MODELS FOR LANGUAGE
Diffusion Models (DMs) -Sohl-Dickstein et al. 2015 Ho et al. 2020 Song et al. (021) have
revolutionized generative modeling, particularly in continuous domains like images (Rombach et al.
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GPQA (8-shot) Generate Length=1024, Acc.=31.47%
LLaDA
TPS: 1.60 tokens/sec
LLaDA
TPS: 25.00 tokens/sec
Acc.=31.47%%
Slow-Fast
Speed Up 15.63x
TPS:48.80
Acc.=30.113 13%
LLaDA
tokens/sec
Speed Up 30.50x
Slow-Fast
dLLM-Cache
TPS:54.75
Acc.=22 alc.:28..999
tokens/sec
Speed Up 34.22x
Throughput and accuracy comparison on GPQA (8-shot, 102gth=1024) on LLaDA with method, including (1) vanilla decoding, (2) SlowFast Sampling, and (3) SlowFast Sampling further enhanced by dLLMM-achhe. Compared to the vanilla setting, SlowFast Sampling alone achieves 15.63 speedup while maintaining comparable accuracy. With dLLM-Cache throughput improves further to 54.75 tokens/sec (up to 34.22 speedup), with only minor drops in accuracy.
Figure 2:
2022, Peebles & Xie 2023; Ma et al.) 2025b). However, adapting these models to discrete data such as text presents unique challenges due its discrete nature. promising approach in discrete diffusion models involves Masked Diffusion Models (MDMs) (Austin al. 202 Lou al. 2023
Shi et al. 2024 Nie et al. 22025a Hoogeboom et al.) 2021 Campbell et al. 2022). which iteratively predict masked tokens based their context. These advancements have transformed text generation, offering compelling alternative to autoregressive paradigms in LLMs. Notable examples include LLaDA Nie et al. 2025b). an 8B MDM trained from scratch with bidirectional Transformer, and Dream Ye et al. 2025), which initializes from pre-trained ARM weights. Both models demonstrate performance comparable similarly-sized ARMs like LLaMA3 8B (Dubey et al. 2024). Their bidirectional architecture may overcome ARM limitations such as the reversal curse (Berglund et al. 2023). making diffusion a competitive alternative for foundational LLMs.
2.2
ACCELERATION METHODS FOR DIFFUSION BASED LLMS
The high inference latency of dLLMs, primarily due their iterative denoising process (Nie et al.) 2025b Ye et al., 2025). has spurred research into acceleration techniques. Various strategies have been developed. mainly including caching mechanisms and advanced sampling techniques.
Caching Mechanisms. Feature caching reduces redundant computations by reusing intermediate features. dLLM-Cache (Liu 2025) combines long-interval prompt and short-interval response caching with a V-verify mechanism for faster inference. Sparse-dLLM (Song et al. (2025) applies dynamic cache eviction with sparse attention, retaining only salient tokens to cut memory and boost speed. dKV-Cache (Ma et al., (225aa) adopts delayed KV caching to reuse decoded tokens' representations, achieving 2-10x acceleration.
Advanced Sampling Techniques. Optimizing the sampling process itself is another major direction for accelerating dLLMs. Low-confidence remasking (Chang et al. 2022 Nie et al.) 2025b) prioritizes high-confidence tokens speed up convergence; semmi-autoregressvve (Nie et al. 2025b Arriola
et al. 2025) remasking divides sequences into blocks, applying random and low-confidence strategies. Additionally, GtR (Yan et 2026) accelerates MAR models through hierarchical structure- de-ttll sampling strategy, while exact simulation methods for MDMs like the first-hitting sampler (Zheng et al. 2024) have made progress in reducing sampling steps enhancing per-step efficiency.
METHODOLOGY
3.1 PRELIMINARY
Inference Process of Diffusion Large Language Models. Diffusson Large Language Models (dLLMs) generate text (.1,..., ,9L) from a prompt c (CI...., C.M) through an iterative denoising process. The model refines an intermediate state (*) T over discrete steps, from k N 0, where T is the token vocabulary. The process begins with fully masked sequence:
(N)
[MASK] ..... [MASK]
(1)
L times

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Published as a conference paper at ICLR 2026
where [MASK] T is the special mask token.
At each step & E {N, N ,....,,I 1}, a mask predictor pa estimates the original clean sequence ro = (ro,1....,, To, from the noisy state y(h) and prompt c:
Po(rolc,y())
(2)
An estimate of the clean sequence at step k; ((k) is obtained through greedy decoding:
(k)
arg max Po(ro. (*)) Vi E (1....,L)
0,
SET
Although the predictor Pa can decode all masked tokens [MASK] in one step, to ensure high-quality generation, dLLM adopts multi-step decoding process. At each step, the remask strategy refines the tokens. The transition to the next state y (k-11) is governed by sampling strategy S:
((k-1) S(1(") (()), k)
(4)
This iterative denoising process is sampling procedure. Our work aims to improve the sampling efficiency of dLLMM inference, which can be computationally expensive due to the N sequential steps. Using LLaDA (Nie et al. 2025b) as an example, we describe its core sampling strategies. In LLaDA, time steps are defined b k/N and 1 (k 1)/N.
LLaDA explores various strategies for the transition function Equation which differ mainly in how tokens from *(()) update (A) to form (*--1), especially for positions that were [MASK] in (());
Random Remasking. this strategy, for each position é:
If * [MASK], then y/3 (8-1) (k) (known tokens are preserved).
=x and remains [MASK] with probability & ensuring the expected number of masked tokens aligns with the noise schedule.
y [MASK]. then y, [8-1) is set to a(.) with probability
Low-Confidence Remasking. This deterministic strategy aims to improve sample quality by selectively unmasking tokens. For each position i, if y = [MASK], the model predicts a(8) and computes its confidence. Specifically, the confidence is given by:
C Po (o,
(5)
(k) If Vi
* [MASK], then G; 1.
The target number of unmasked tokens for state (k-1)) is given by:
- N
7un = [L(1 +--1) I
(6)
The tokens corresponding to the nun highest confidences are unmasked in (k-1), while the remaining positions to [MASK].
Semne-uutoregresivve Remasking, This strategy is used in LLaDA after Supervised Fine- Tin-TTning. The sequence is divided into blocks, and generation proceeds block by block from left to right. Within each block, the random remasking or the low-confidence remasking strategy is applied iteratively to denoise the block before moving the next.
The choice of sampling strategy S, along with the number of steps N, significantly affects both the generation quality and latency.
3.2 THREE GOLDEN PRINCIPLES OF DLLMS
Building upon the iterative denoising process outlined in Section 3.1 behavior, particularly with models like LLaDA, has revealed consistent patterns in token generation. These patterns, which term the Three Golden Principles, form the bedrock of our proposed acceleration strategy. They describe how token certainty, convergence, and positional effects interact during the diffusion process, offering key insights into optimizing sampling.
empirical analysis of dLLM
The Certainty Principle: High Confidence Indicates Determination.
We observe that tokens predicted with high confidence by the mask predictor po significantly more likely to be part of the
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final, correct sequence and tend to remain unchanged in subsequent denoising steps. The confidence for a predicted token A at position and step k is given by (o c.. (()).. As illustrated in
Figure (bb, any given step subset of tokens typically exhibits substantially higher confidence scores compared to others. These high-confidence tokens are prime candidates for early "acceptance"
or less frequent resampling.
Implication for Acceleration: By prioritizing tokens that quickly reach high confidence threshold, we can reduce redundant computations on already determined parts of the sequence.
The Convergence Principle: Tokens Stabilize Over Iterations. During the iterative refinement
(k)) process, individual tokens (both their predicted identity P(.) and their confidence Pe (F |c, y y())) )
undergo a period of fluctuation before settling. As shown in Figure (a), representative token might initially change its predicted identity and confidence across several early diffusion steps. However, as k decreases. the token's 's confidence converges are often to a stable value. This convergence signals that the model has formed a consistent belief about the token's 's identity within its current context.
Implication for Acceleration: Tokens that have demonstrated convergence (i.e., stable identity and confidence over a window of recent steps) are less likely to change. Aggressively decoding can prevent unnecessary re-evaluation, thereby speeding up the process.
The Positional Principle: Decoding Exhibits Regional Preferences. Beyond individual token behaviors, we find that the generation process often exhibits spatial patterns. High-confidence and early-converging tokens do not appear randomly scattered throughout the sequence. Instead, they frequently emerge in contiguous blocks localized regions, suggested by Figure [ (b). This phenomenon might be due to local semantic dependencies or the influence of strongly contextualized parts of the prompt c. For example, after few initial steps, particular span of tokens might collectively achieve high confidence and stability, while other regions remain largely masked or uncertain. The model appears to focus its decoding efforts on specific segments at different stages.
Recognizing these regions allows for targeted decoding. Instead of uniformly processing all tokens, computational resources can be concentrated on regions that currently most amenable to decoding.
Implication for Acceleration:
These three principles collectively indicate that one-size-fits-ahe static sampling strategy is inherently inefficient. They motivate a dynamic approach where the number of tokens sampled, their selection criteria, and their locations are adapted based on the evolving state of the generated sequence. Our SlowFast Sampling methodology, detailed in Section 3.3 is designed to explicitly leverage these observations to achieve significant inference speedups.
3.3 SLOWFAST SAMPLING
1. Exploratory Stage (Slow Phase): Identifying the Next Stable Region. As illustrated in Figure 3 the primary goal of this stage is to cautiously advance decoding while identifying a promising, stable region for subsequent rapid processing. Starting from Seyole and extending to the end of the full sequence L. this stage operates as follows for limited number of dLLM steps:
Cautious Decoding: At each dLLM step l within this stage, we perform conservative decoding update. We select the top-kstow tokens within the current exploratory window [Seyate; L] that exhibit the highest confidence (a(())
(k) |c, (()),, and these are unmasked to form part of (k) (as per Equation for these tokens, followed by Equation 4
End Point of Convergence Prediction: Concurrently, the model predicts end point of candidate convergence, < This is defined as the furthest token position i E [Seyates I.] for which the predicted confidence Po (f cc, (+)) exceeds a minimum confidence threshold Tmin_conf- Mathematically:
(k) max {i| [Seycles L) Pa (F(
c, (()) > Tmin_conf}
cand
This cand represents an estimate of how far into the sequence the model can confidently decode at the current step k:
Stability Check: Throughout the Exploratory Stage, candidate convergence horizons e pre-
dicted at each dLLM step are tracked. Let Hw Hw(k)) (!) I [max(11 (1, ki W + 11, ks.]]
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Slow
history window threshold:O.A. 23 incex
Fost
coche
Topk
only compute
step
[s_cyylle e_cyl
End Point Convergence Prediction
vor=O, 25 2
next predict
step k-1
index 7 index B
step k-3
Topk
StEp step
parallel decoding
var=0, 22-threehaaldd0...
step
findxx
index index
step K-4
Topk
step k step k-1
step k-2
confidence map
Sid cautious decoding
7 parallel decoding
End point of Convergence
cached token
Figure 3: Overview of the SlowFast Sampling Pipeline. The method alternates between a Slow (Exploratory) stage and a Fast (Accelerated) stage for efficient token generation. In the Slow phase (left), the model conducts cautious decoding by selecting top-k high- high-confidence tokens per step while continuously predicting the End Point of Convergence and calculating confidence variance across history window. Once variance drops below threshold (e.g., 0.22- < 0.23), the corresponding region [Scycle, Ccycle] is considered stable. In the Fast phase (right), this stable span is decoded in parallel with aggressive unmasking of high-confidence tokens, while tokens beyond the span are temporarily skipped and their results cached for reuse. This alternating structure reduces redundant computation and accelerates decoding while maintaining output quality.
be the set of candidate horizons in the sliding window of the latest Whist predictions ending at exploratory step k, Stability is achieved, concluding this stage, when Var(Hw (k,)) < dttable for a window where ki 2 W hist. The exploratory stage ends at step k f defined as:
(8) If the stability criterion is not met by Kmaz (the maximum allotted exploratory steps), then fiinal = K Man-- The cycle's convergence horizon Ccyele, is then set to the mean of the candidate horizons in the final window Hw (kntna)):
kfinal = min ((ks Whist S Kg S Kmaz A Var(Hw (k:,) < Etaateed U {Kmer)) })
Ccyele = Mcan(Hw (kfimal)) =
e
(9)
HH (keamal) I
eE (keyonat)
Once this stability criterion is met, the Exploratory Stage concludes. The endpoint for the subsequent
(k) Accelerated Decoding Stage, Ecyole, is set to the last recorded candidate horizon, 'eand- If stability is not achieved within a maximum number of exploratory steps, Ecycle can be set to a conservatively
determined position or the process might default to a full-sequence cautious decode for that cycle.
Accelerated Decoding Stage (Fast Phase): Rapid Parallel Refinement, As shown in the right half of Figure 3 once a stable region [Seycie, Ceyelel is identified, this stage aims to rapidly denoise tokens within this span, while efficiently handling tokens outside it:
Out-of-Span Caching: For tokens outside the identified span (i.e., positions > Ecycle). if their
..()) current predicted confidence is low (e.g., below Tmin_con their predicted values fo.s from one dLLM step within this stage are computed and then cached. These cached values can be reused in subsequent dLLM steps for these positions, provided they remain outside an active decoding span, thus saving redundant computations.
In-Span Parallel Decoding: Within the span [Soyctes Ccyote]. an aggressive parallel decoding is attempted. All tokens
yi (b) [MASK] for i E [Seyetes Ccyete] for which the predicted confidence Po(a(h) c (()) exceeds the high certainty threshold Trigh_ommf: Le.,
Po (o 0.i |c, (10) > Thigh_comf
(10)
are unmasked by setting their value to the corresponding prediction f This update is performed simultaneously for all such qualifying tokens in a single conceptual step to form ((k)

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Published as a conference paper at ICLR 2026
Table 1: Performance of LLaDA 8B and Dream 7B with Flot Sampling on 8 benchmarks.
Task
Method
Inference Efficiency
Performance
Method
Inference Emeiincy
Performance
TPS:
Speed
Score]
TPS1
Speed1
Score"
Mathematics Sclence
LLaDA
4.55
1.00%
69.83
Dream
8.16
1.00x
77.02
GSMBK
Fast-dLLM (Panallel)
7.45 00
09.60..0 oz
Fist- dLLL (Paraliet)
12.72
1.55
73.09
Fust
14.57.10002
3.20%
69.59
+ Sllow Fast
17:15 5,00
2.10
76.50 0..2
LLaDA
3.51
1.00x
31.47
Dream
5:43
1.00%
35.93
GPQA
Fast-JLLM (Pamallel)
11.72...41
3.54
32.13.
Fass-dLLLM (Purallet)
15.88
4..2
31.01-4.0
SlowFust
16.36,1305
4.94
31.91
+ SSoow Fast
16.56 11:33
3.05
35.944
LLaDA
5.14
1.00%
30.16
Dreum
8.48
1.00x
38.68
Math
Fast-dLLM (Punilee)
30.52,00:1
L.M (Paratiel)
22.51 14:5
2.65% 1.
4.00
StowFast
11.27...14
2.19%
29.64 p.52
Sloww Fast
23.00
2.71 21.71
36,
General Tiokks
LLaDA
9.16
1.00x
23.30
Dream
14.97
1.0x
24:14
MML
+ Fast-dLLM (Panallal)
14.62
1.7mX.umm
23,50,0.20
Fist-dLLLMM LM (Paraliel)
25.50
1.70x
19.53
SlowFast
23.14,13.0
2.53K....5
23.85
-Sloww Fast
22. 22.80..7.43
1.52
22.91
LLaDA
5.02
1.00x
62.11
Dreom
8.46
1.00%
72.61
MMLU
Fost-JLLMM (Punallel)
12,96.200
2.58x.1.6
61.67 -044
Fast dLLM (Parallet)
17.39
2.05
64.55
SlowFast
1681 79
3.35% y
66.56 46
SSoowFast
18.43 9.97
2.18% y1
75.13 y9
LLaiM
4.04
1.00%
44,97
Dream
6,93
1.00x
51.83
BRH
Fast-A1 (Panillet)
10.78,0.00
2.66X.1..
44.13
Fist-di I.M (Paralial)
27.84-30.51
4.01
52.28,0045
Flow
11.19
4.24
44.60 44.60.0.37
-Slow -SlowFasd
28.14
4,06 1066..33 23.00
50.55 1.30
Code
LLabA
4.98
1.00x
Dream
1.00x
$4.20
MRPP
+ Fast-Af I (Panitel)
40.80,0..o0
Fist-df L.M (Paraliel)
22.87-1345
2,56 y
49 29.40
SlowFast
13.32,434
2.678.107
41.00.0.00
Slow Fast
29.07
a.
S4.
HamanEval
LLDDA
11:24
1.00%
31.71
Dreum
11:49
1,00x
34.15
+ Fast-dLLM (Panallel)
23.05
2.08
32.32
Fast dLLM (Paralieb)
24.33
2.12%
32.92
SlowFast
35.46,20:22
3.15
33.54 23.43
SSlow Fast
25. 38 13.49
2,21 21
35.36,1.21
Fallback Top-k Refinement: If the number of tokens meeting the Thigh_comt criterion within the span insufficient to make substantial progress (e.g., less than one), we revert to a more conservative update for this dLLM step within the span. Specifically, we select the top-kfast tokens within [Scyele Ecyete] based on confidence and unmask them. This ensures steady progress even when widespread high certainty is not yet achieved.
After the Accelerated Decoding Stage completes, the starting position for the next cycle's Exploratory Stage is updated to Seyele - Ecyele. This cyclical process of exploration and accelerated decoding continues until the entire sequence is generated. This SlowFast approach dynamically adapts the decoding focus and intensity, leveraging the Certainty and Positional principles to identify promising regions and the Convergence principle to stabilize them efficiently.
4 EXPERIMENT
4.1 EXPERIMENT SETTINGS
Implementation Details To evaluate the effectiveness of our proposed dynamic sampling approach SlowFast Sampling, we conducted experiments on representative dLLMs: LLaDA 8B (Nie et al. 2025b) and Dream 7B (Ye et al.. 2025), focusing on measuring the inference acceleration across various benchmarks. All experiments were conducted on NVIDIA RTX 4090 GPUs
Evaluation Metrics We evaluated sampling acceleration and generation quality of SlowFast Sampling using quantitative metrics. Inference speed is measured in Tokens Per Second (TPS), indicating the average number of tokens generated per second. Generation quality assessed using task-specific metrics, e.g., accuracy on GSM8K, reflecting the model's performance under inference acceleration.
4.2 MAIN RESULTS
Performance and efficiency gains across models. Table Trpport throughput and model perfor- mance for both LLaDA 8B and Dream 7B, with and without SlowFast Sampling. These results demonstrate that our method brings significant improvements in inference efficiency and achieves loss- less acceleration in most cases. In our experiments, the key hyperparameters of SlowFast Sampling, Ff and Thigh_ were set to 0.1 and 0.85, respectively. The remaining hyperparameters in our method were set follows: maximum exploratory steps Knaz = 8, sliding window size
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Table 2: Performance of LLaDA 8B and Dream 7B with Slow Sampling and dLLM-Cache.
Task
Method
Inference Elfiiince
Performance
Method
Inference Emiieney
Performance
TPS1
Speed/TPSI1
Scoret
TPS!
Speed/TPSit
Scoret
Mathomutics Science
LLnDA
4.55
1.00x
69.83
Dreast Itsd-dt2 Caatil
1.00x
77.02
GSMBK
Fost-dLLM (PanlleteCache)
15.50-1356
1.4 141X-2.1
68.77-1000
31.00
-81x-253
69.5 1.00
StowFist Cache
26.99
5,93X
69,00
Slow Fust e Coche
46.17
5.66X
72.10
HLLDW
3.31
1,00X
31,47
Dreast
5:45
1.00x
35,90
CIQQ
Fist dLLM (PacalelleCchee)
30,21-2150
-.13
33,00
Fist dLLM (Parzllet Cache)
40.98.10..8
-.54
32.57
Show Past Caclar
29.06
5.78 8.78%.2.0
33,48
Slow Find + Cache
39.63
7,50x
34.82
5.14
1.00X
90.16
Drean
8488
1.00c
38.68
Mar
Fist diLM (aacallleeachhe)
28
Fost dLLM Cachel
46 46,80
33.24
SlowFFstt Cache
5.16
29.42
Slyw Cuche
37.10
Generd Taasks
9:16
23.30
14:90
24:14
MMLU pro
dILM Caacalel
21,8.11 00
1.0XX-2254
27.06,430
dLLM Caacel
42.73
Slaw Cacle
33.38 14
3.64K 13
25.53 po
Slow Fint Cuche
43,50 14.0
2.90 y1
21.81
3.00xx
62.11
Dreas
1.00s
72.61
MMLU
Fint-dLEM CaaceleteCachee
$2.9655754
45.45
68.45.1.00
Fiud-dt J.M Carhll
44. 44.73-3827
4.28
Show Fast + Cacher
$8.42
7.65%
61:20 com
Slow Foet + Cacle
44. 18 19
5.20%
71.57
BBH
11 Fist dLLM (ParllleleCache))
4.06
1,00%
44.97
6:95
1.0 1.00x
$1.81
22.56
5,58x
.5.35.1.00
Fist dLLM (Parzllel Cache)
57.02
8 6.22
44.95
Show Fist Cucha
36.04
$.92
44,81
Slow Find + Cachee
70.20
10.15K
48.24
Cudee
4:98
1,00X
40,80
Droan
8:92
1. 1,00g
$4.20
MBPP
Fist diLM (aatell
22.18.37.0
4,45
34,20
Fost dLLM Cachel
50.40
Fist Cacle
27.26.02.3
-.47
39,00
Slow Cuche
69.48.4..4
4.79
$1.00
18.24
38.71
Drean
11.49
36.15
Humontrca
29.88 1,40
49.6
Fioo Cache
41.14 2ar0
3,66K ya:ss
31,10 nin
Flut Cuche
47.86 2m
4.17% 1
35.36 1:0
Sampling Strategy
1PS
SSoref
Method
TPS
(PP)
Accuracy
Autorogressive (AR)
5.25
60.80
LLaMA3 (Dubey et
2024
33.79
21.12x
31.92
Diflusion Sampling
4.55
69.83
LLaDA
1.60..22.1
1,00x
31,47
Semi- eumi-Autooeeresssvve
5.44
66.41
+ SlowFast
Slow Cache (K₂ 100, K, 5)
48, 415.01
25.00 ...9
30,50x
15.63
31.47-0.45
30.13
SlowFast Sampling
9.87
69.59
SlowFast Cache (R2 500, K, 30)
54.75 (20.00
34.22x
28.79 8.18
Table 3: Comparison of Sam- pling Strategies on inference efficiency and generation per- formance.
Table 4: Comparison of LLaDA 8B Base with other represen- tative LLMs. Compared to LLaMA3 8B, LLaDA with SlowFast Sampling and dLLM-Cache achieves significantly higher through- put (up to +20.96 TPS) while maintaining comparable accuracy.
Whist- = 2, and stable variance threshold 2 stable = 1.0, consistent with the default configuration used in Section 2 and ablation studies.
Compatibility with dLLM-Cache. Recently, several studies have explored leveraging feature cache to reduce the computational cost of dLLM inference. Our SlowFast Sampling is highly compatible with existing caching mechanisms and the integration can lead to higher acceleration compared to using either alone. Table 2 compares the performance and inference speed of LLaDA with and without applying our method in combination with dLLM- MCCahh. The results show that this integration can deliver higher throughput while maintaining comparable model performance.
Comparison with Other Sampling Strategies. We compared our method SlowFast Sampling with four alternative sampling strategies: diffusion sampling. diffusion eumi-eutoreeressivv sampling, autoregressive (AR) sampling and Fast-dLLM (Wu et al.) (025). Diffusion sampling adopts a remasking strategy to iteratively select token to decode parallel, while AR sampling generates tokens strictly from left to right. The semi-autoregressiv approach generates blocks left-to-right and applies the remasking strategy within each block. LLst- in contrast, performs parallel decoding only based token confidence, and we report results for both its parallel and cache- augmented variants. As shown in Table 2 and 3 our method SlowFast Sampling achieves higher inference efficiency while maintaining competitive generation quality.
4.3 ABLATION STUDY
Case Study of SlowFast Sampling in Sentence Generation. As shown in Figure 4 this case study shows how SlowFast Sampling generates text by alternating slow and fast phases. In the slow phases, the model outputs few reliable tokens such subjects, verbs, or punctuation to anchor the sentence. The fast phases then produce longer spans of high-confidence tokens in one step, e.g., "she has 9 4 = 5 yuan left", ensuring both efficiency and correctness. This balance of cautious slow decoding
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Published as a conference paper at ICLR 2026
and confident fast decoding maintains accuracy while accelerating generation, making the approach intuitive and effective.
Prompt: Jano sold apples of juan ooon, then bought 2 poars 2 yoan ocon. How rouch money does sho have lof?
Resporise Janw acld 5 apples les at 3 yuan with, so shi sommed 5 5 yuan. She bought? 2 paara at yuan wachh, so she spand 2 yuan Therwoow, shs Iss 2-4 yuar bef.
token
stop
point
Tost phase
end poin1
fest phase
and point
Jano_ Jane sold apoles at., yuan -n Jano sold apples at yuan each
bis font the Inkera decoded in cument stwp
point
(80 the normed 3 90 eamed. 3_ =
90 samed_ = yuan 50 she earred 3"" 3 yuan.
each bought. poans yunn ench, so spent 2" yian in bought. peans at., 2yua each, so she spent 2 2
point
spont 2 2 Thersfore - she bought 2 pears at yuan coch, so she spent 2" yuan Thorefore, she has 9 yusa lont
Thersfom in the bought 2 peans at 2 yua each, no the spent. 2 2 4 yuar. In Therefor
she bought 2 peaes at yuar enon, so she
yuan
len1 the bought. 2pcars at each each she spent _ .2 yuon. Therefore, she has
Figure 4: Case study of sentence generation with Slow Sampling, The figure illustrates the three phases, where exploratory and accelerated
dynamic evolution of the confidence map decoding are alternated. Blue tokens denote those generated in the current step, highlighting how the method gradually builds on high-confidence spans to produce coherent final response.
Effect of Hyperparameters in the Stability Check. The stability check, which transitions the model from the slow to the fast phase, is governed by three hyperparameters whose effects are shown in Figure 5 The maximum number of exploratory steps, Kaazi is for allowing the convergence horizon to stabilize; we find Kmax = 8 provides sufficient exploration for high-quality generation without incurring excessive overhead. This is complemented by the sliding window size, Whist- Since prediction changes and convergence occur rapidly, we find smaller window of Whist effective, striking a strong balance between maintaining quality and maximizing inference speed. Finally, strict stable variance threshold of o stable 1.0 ensures that the accelerated phase is only triggered for genuinely stable regions, solidifying the reliability of our method.
14.6
15
Original Acc.
Accurey (OSMBK)
69
Original Acc.
14.4
Accuncy (GSMBK)
70
Original Acc.
14
(GSM8K)
69.5
68
14.2
IP9
69
13
Accuray TPS
13
12
TPS
Appuracy
13.8
67
Acsuracy
TPS
TPS
Appuracy
11
66
13.6
10
68
10
1.5
2.5
exploratory stops Ka..
Stable Varience o zzabte
Sliding Window W wext
Figure 5: The sensitivity study on hyper-parameters in the stability-Check Accuracy and TPS
vary with Kman. o² stable and W hist- The chosen defaults (Kmax 8, 02 stable 1.0, W Whist 2)
offer strong speed-quality trade-offs.
Outperforming Autoregressive LLMs in Inference Speed. As shown in Table when equipped with our SlowFast Sampling and dLLM-Cache, LLaDA Base not only accelerates significantly over its default setting but also outperforms the autoregressive LLaMA3 8B (Dubey al. 2024) in inference speed (54.75 vs. 33.79 TPS), while maintaining comparable accuracy. This demonstrates that di dLM,s, with proper sampling and optimization, can surpass traditional autoregressive models in both efficiency and practicality.
CONCLUSION
In this work, we present SlowFast Sampling, a dynamic and principled approach to accelerate diffusion-based large language models (dLLMss. By leveraging three key observations: token certainty, convergence, and positional bias, we design a two-stage decoding pipeline that adaptively balances exploration and efficient parallel decoding. Extensive experiments across benchmarks demonstrate that our method not only significantly improves inference speed (up to 15.63x x and up to 34.22x combined with BLLM-Cachh quality, outperforming even autoregressive LLMs in throughput. We believe this work marks an important step toward making di ILM practical and competitive in real-world deployment.
the GPQA benchmark), but also maintains strong generation

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Published as a conference paper at ICLR 2026
ACKNOWLEDGEMENT
This project is sponsored by CCF-Tencent Rhino-Bire Funds.
REPRODUCIBILITY STATEMENT
We have made every effort to ensure the reproducibility of our results. The main text (Sections 3 provides detailed descriptions of the proposed SlowFast Sampling method, its integration with dI.LM- Cache, and the corresponding experimental setups. Hyperparameter configurations and additional implementation details are presented in Appendix [A.3 Comprehensive performance comparisons across benchmarks are reported in Tables T Ablation studies and a case study are provided in Section 4.3 (e.g.. Figure to illustrate the robustness of method. To facilitate replication, include the source code and scripts for running experiments in the supplementary materials.
ETHICS STATEMENT
This work focuses on developing efficient sampling strategies for diffusion-baset large language models (dLLMs). Our research does not involve human subjects, personal sensitive data, or applications directly impacting individuals. All experiments were conducted on publicly available benchmarks, ensuring fairness, reproducibility, and transparency. We release code promote open research and to enable verification of our results. We are not aware of any potential misuse or harmful applications of the proposed methods beyond standard concerns inherent language models, such bias or misuse downstream tasks, and we encourage responsible use in line with the ICLR Code of Ethics.
REFERENCES
Marianne Arriola, Aaron Gokaslan, Justin T Chiu, Zhihan Yang, Zhixuan Qi. Jiaqi Han, Sub- ham Sekhar Sahoo, and Volodymyr Kuleshov. Block diffusion: Interpolating between autoregres- sive and diffusion language models. arXiv preprint arXiv:2503.09573 2025.
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems, 1111199881199999 2021.
Lukas Berglund, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, and Owain Evans. The reversal curse: Lims trained on" is b" fail to learn" b is a". arXiv preprint arXiv:2309.12288 12288, 2023.
Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, and Amaud Doucet. A continuous time framework for discrete denoising models. Advances Neural Information Processing Systems, 35:28266-28271 2022.
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative image transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1131--1325,, 2022.
Abhimanyu Dubey, Abhinay Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang. Angela Fan, al. The Iaaaa herd of models. arXiv preprint arXiv:2407.21788 2024.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling, Argmax flows and multinomial diffusion: Learning categorical distributions, Advances in Neural Information Processing Systems, 14:12454-11444 2021.
10

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Published as a conference paper at ICLR 2026
Zhiyuan Liu, Yicun Yang, Yaojie Zhang. Junjie Chen, Chang Zou, Qingyan Wei, Shaobo Wang. and Linfeng Zhang. dilm-cache: Accelerating diffusion large language models with adaptive caching, 2025. UtLpttps::///// /ttttp:://qttulte com/ hwtm///ammmcccnnn//cccaae wLtpeeecuunnnnLL-aaa-a cache
Aaron Lou, Chenlin Meng, and Stefano Ermon. Discrete difftusion language modeling by estimating the ratios of the data distribution. arXiv preprini arXiv:2310.16884,, 2023.
Xinyin Ma, Runpeng Yu, Gongfan Fang, and Xinchao Wang. dkv-cache: The cache for diffusion language models, 2025a.
Yue Ma, Kunyu Feng, Zhongyuan Hu, Xinyu Wang. Yucheng Wang. Mingzhe Zheng, Xuanhua He, Chenyang Zhu, Hongyu Liu, Yingqing He, et al. Controllable video generation: A survey. arXiv preprint arXiv:2507.16888 2025b.
Shen Nie, Fengqi Zhu, Chao Du Tianyu Pang, Qian Liu, Guangtao Zeng, Min Lin, and Chongxuan Li. Scaling up masked diffusion models on text, 2025a. URL httts::/arxii.. org. abs/
2410. 18514
Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, and Chongxuan Li. Large language diffusion models, 2025b. URL https
//aaxxi..rc//ab///5522... 09992
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp. 1105,4205, 2023.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis K Titsias. Simplified and generalized masked diffusion for discrete data. arXiv preprint arXiv:2406.0445 2024.
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference machine learning. pp. 2256-2265. PMLR, 2015.
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Interna- tional Conference on Learning Representations, 2021.
Yuerong Song, Xiaoran Liu, Ruixiao Li, Zhigeng Liu, Zengfeng Huang, Qipeng Guo, Ziwei He, and Xipeng Qiu. Sparse-dllm: Accelerating diffusion I1ms with dynamic cache eviction, 2025. URL
lattps://axxi: wwwpsii..ogg/ab/////// 02558
Chengyue Wu, Hao Zhang, Shuchen Xue, Zhijian Liu, Shizhe Diao, Ligeng Zhu, Ping Luo, Song Han, and Enze Xie. Fast-dllm: Training-free acceleration of diffusion Ilm by enabling kv cache and parallel decoding, 2025. Bttthttss://axx.
org/ abs 211./555..2222
Feihong Yan, Peiru Wang, Yao Zhu, Kaiyu Pang, Qingyan Wei, Huiqi Li, and Linfeng Zhang. Gen- eration then reconstruction: Accelerating masked autoregressive models via two-stage sampling, 2026. URL
251 1 17171
Jiacheng Ye, Zhihui Xie, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, and Lingpeng Kong. Dream 7b, 2025. URL //kkupl:////kppppp. qi tuut..ub... 10 /blc//ll/////// 2025/ dream
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian Yun Nie and Ji-Rong Wen. A survey of large language models, 2025. URL https: http://aaavvvvv.. org/bbs/
2303. 18223
Kaiwen Zheng, Yongxin Chen, Hanzi Mao, Ming- Yu Liu, Jun Zhu, and Qinsheng Zhang. Masked diffusion models are secretly time-agnostic masked models and exploit inaccurate categorical sampling. arXiv preprint 240902240,,02998, 2024.

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Published as a conference paper at ICLR 2026
A APPENDIX
A.1 LLM USAGE STATEMENT.
We used a Large Language Model (LLM) solely to aid in polishing the writing and improving the clarity of language. The LLM was not involved in research ideation, experimental design, data analysis, or generation of scientific content. All research ideas, methods, analyses, and conclusions presented in this paper are entirely the work of the authors. The authors take full responsibility for the content of this paper.
A.2 COMPATIBILITY WITH ADVANCED SAMPLING METHODS.
Figure 6: Effect of Con- fidence Thresholds on GSM8K. Tmin_conn controls the exploratory range, and Thigh_con/ balances accuracy and speed during fast decod- ing. Other hyperparameters follow the default settings in Section 4.2
70
14.7
Accunty (G5M77))
69.5
Original Acc.
14.8
14.5
Accurott (S5W6R)
Original
09.5
14.4
16.3
63
g
67.6
14.2
M6.1
Agpunnyy
14
Arc many
TP8
13.9
TFS
13.8
0.7
0.75
3.8
0.85
0.80
Miniuum Confidence Threshold Fasis_cond
High Certainty Threshold Vargh
Sensitivity to Confidence Thresholds. The confidence thresholds, Tmin_con, and Thigh_cons dictate the behavior of our SlowFast pipeline. As shown in Figure 6 the minimum confidence threshold Tmin__onp defines the candidate region in the exploratory stage. A moderate value of Tmin_coom 0.1 is optimal, as lower values lead to unstable regions and higher values create overly conservative, inefficient regions. The high certainty threshold Thigh_coms directly manages the speed- quality trade-off during the accelerated stage. A higher value leads to more cautious and accurate generation the cost of speed (TPS). We select Thigh_eony 0.85, which achieves a near-peak GSM8K score without drastic reduction in inference speed, striking an effective balance.
A.3 EXPERIMENTAL DETAILS
To ensure fair and reproducible comparisons, we standardize the setting of inference generation across benchmarks and each model with its officially recommended settings. The benchmark-specife parameters considered include the number of inference steps, the block length, the total generation length, and the number of few-shot examples. For clarity, summarize the settings for LLaDA and Dream in Tables 5 and respectively.
Table 5: Experimental settings for LLaDA across benchmarks.
Steps
Block Length
Generation Length
Few-shot
MMLU
MMLU-pro
256
256
256
0
GSMNK
256
256
256
4
Math
256
0
GPQA
256
256
256
5
HumanHival
256
256
256
O
MBPP
256
256
256
3
Table 6: Experimental settings for Dream across benchmarks.
Task
Steps
Generation Length
Few-shot
MMLU
5
MMLU-pro
256
0
GSM8K
256
256
8
Math
256
256
4
GPQA
256
256
5
HumanEval
256
256
O
MBPP
256
256
3
BBH
256
256
3
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Published as a conference paper at ICLR 2026
A.4
ABLATION STUDY ON HYPERPARAMETERS AND ROBUSTNESS.
Accuracy(Math))
Accuracy(MBPP)
Accurasy(HumanEval)
TPS(Math)
TPS(MBPP)
EPS(HumanEval))
.55
40
55
40
40
50
Our setting
50
80
30
Our setting
Our setting
Accurall
44
Accurec
45
Accuraccy
45
40
20
TPS
40
20
TPS
20
10
10
10
25
25
0.050.00 370
0.25
0.3
0.35
0.75 0.8 0.85
0.9
0.95
0.5
1.5
3
3.5
Minimum Confidence Threshold Vmin,con/
High Certainty Threshold thigh.
Stable Varience o Databl
Figure 7: Parameter Robustness and Sensitivity Across Multiple Benchmarks. Figure shows sensitivity across MATH, MBPP, and HumanEval. Our setting is shown to be highly robust across all tasks.
17.5
Accunccy (GSWBK)
Our setting
Accuracy Accuracy (GSMBK)
Our setting
17
Accuray ( (GSMBK)
Our setting
76
17
78.5
76.5
17
16.52
78
16
TP8
g
14
16.5
72
16
Accuracy
75.5
Accuracy
TPS
13
TPS
70
15.5
75
12
75
16
0.050.07 0.1 0.15 0.25 0,3 0.35
0.7
0.95
1.5
Minimum
Threshold monn
High Certalinty Threshokl Taigh.com/
stante
Figure 8: Parameter Stability Across Model Variants on Dream 7B. Sensitivity analysis for
Tmin_connf fo Thigh_eonff and x2 sttalle was conducted on Dream 7B using the GSM8K benchmark. The findings confirm that the unified set of hyperparameters successfully transfers to this demanding model variant, validating overall parameter stability.
Hyperparameter Robustness and Generalization Analysis. The overall hyperparameter analysis confirms the robustness and strong generalization ability of the SlowFast approach. As shown in Figure 7 single, unified parameter set maintains near-optimal performance across diverse benchmarks (MATH, MBPP, HumanEval). This stability is further validated by Figure where the same unified settings successfully transfer to Dream 7B on the GSM8K benchmark. These findings confirm that the SlowFast approach is highly robust and does not require extensive tuning for new tasks or model variants.
A.5
PERFORMANCE EFFICIENC TRADE-OFF ACROSS STRATEGIES
Figure evaluates the Performance- for method against various baselines under two distinct evaluation setups. The results confirm that the integration of our method consistently shifts the trade-off curve decisively towards the top-right corner (higher TPS and higher Accuracy) compared to the baseline. This superiority holds true across both the standard configuration (5- shot, 256 tokens) and the more demanding scenario (8-shot, 1024 tokens), validating the robustness and scalability of our adaptive sampling strategy.
50
40
30
+ LLaDA
!
- LLaDArSioN
10
LLaDArSIow
CashtCeahel
28
29
32
33
Accurancy (GPQA)
Figure 9: Performance Effi-
ciency Trade-off on GPQA.
A.6 CASE STUDIES ON POSITIONAL PRINCIPLE AND NON-CONTIGUOUS DECODING.
Robustness to Minor Positional Uncertainty. Figure 10 confirms SlowFast's ability to cover multiple stable blocks even when separated by unstable regions. Crucially, the distance between the end of one stable block and the start of the next is generally small, allowing our adaptive mechanism to efficiently jump over the intermediate low-confidence span. This ensures that the overall acceleration maintained without sacrificing accuracy, even when faced with minor positional uncertainties.
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Published as a conference paper at ICLR 2026
Prompt: Any bookk diklers sech, then bought jem dolless wach musn monnay dowir ruce
Response: Any adld booka at dollaa sach, whlch 55 dallars, Then ale boughe if pai at dallirs sach, weliiii dellins, So, site has 20- n 18 clalars lan,
tokon
and
East phase
point
Figure 10: Case Study I: Dynamic Span Coverage During Positional Jumps. This study illustrates a scenario where the model exhibits a positional jump between decoded tokens, confirming that the dynamic search window of SlowFast successfully identifies and covers the subsequent stable region.
Prompt: Jane aoid apples of juan each, then bought pears at yuar each Flow much money does she have lnf?? Reeponss: Jane antr anplee 2 your each so ahe wammed 3i yuum. She used yuur to buy peans at yuun each, so she has 4- your helt taken
xtep
block
block
phase
fast phase
Figure 11: Case Study II: SlowFast'' Adaptive Response to Induced Non-Conticn Non-Contiguous Blocks. This case study demonstrates the adaptive capability the SlowFast framework. By manually inducing positional jump, the figure shows how the dynamic adjustment mechanism immediately recognizes and proceeds to the subsequent high- conf-confidence region.
Adaptive Handling of Non-Contiguous Blocks. Figure II rigorously tests SlowFast's adaptive limits by manually constructing an extreme scenario featuring two widely separated high-confidence blocks. The visualization confirms that upon encountering the unstable region between the blocks, the dynamic adjustment mechanism successfully identifies the subsequent high-confidence region (Block 2) and resumes acceleration (Fast Phase 2). This successful navigation demonstrates the robustness of SlowFast's dynamic scheduling, confirming its ability to automatically identify and accelerate multiple non-contiguous stable blocks even under extreme conditions.
A.7 ALGORITHM: SLOWFAST SAMPLING FOR DLLMS
Algorithm 1 outlines the SlowFast Sampling Strategy, dynamic, two-phase framework for accelerat- ing dLLMs. starts with the Slow, Exploratory Stage, which proceeds step-by-step (Lines 10-1 12) to determine a safe decoding span boundary. This boundary is validated by Variance-based Stability Check (Lines 16-22), confirming stability when the variance endpoint predictions falls below 22 stable Once stability is confirmed, the algorithm enters the Fast, Accelerated Decoding Stage. Here, acceleration is achieved via In-Span Parallel Decoding (Lines 40-47), where high-confidence tokens within the span are unmasked simultancously. Additionally, Out-of-Span Caching (Lines 34-39) prepares for the subsequent cycle. This adaptive, two-stage mechanism ensures maximum efficiency achieved only in reliably stable regions.
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Published as a conference paper at ICLR 2026
Algorithm 1 SlowFast Sampling Strategy
Require: Masked sequence y y( Prompt e, Max steps N, Exploratory limit Kmazs History window Whist Stability threshold o stable Confidence thresholds Tmin_conf: Thigh_con.f-
Ensure: Generated sequence (
k <-> N
b Initialize diffusion step Start of the current decoding cycle b History convergence points
2: Seyele <-1
3: Hw +
4: while & > 0 and Scyele < L do
5: Phase Exploratory Stage (Slow)
6:
countocttt <
7: is stable t False
8: while step_cound Kaar and k > 0 do
Predict f(x) and confidence Pa (8))
9:
10:
Cautious Decoding:
11:
Unmask Top-kstow tokens in range [Scyele: L]
12:
End Point Prediction:
13:
cann < max i i E [Seycle LJ A Po(Fo.)) > Fmin__mmf
(K)
14:
Update history window Hw with ecand Stability Check:
(R)
16:
Varcare curr < Variance (Hw)
if Hw! 2 Whist and Varcarr < o dsabll then is_ is_stable +- True
18:
Ceycle t- Mean(Hw)
20:
k 1
21:
break
Exit slow phase early
22:
end if
23:
Update state (²=-1) using standard scheduler k +- k-1
24:
25:
step_counr + step_count + end while
26:
27:
if not is_ s_stable then
28:
Gcyele + cand
b Fallback if no convergence
end
30:
// Phase 2: Accelerated Decoding Stage (Fast)
31:
if then
Predict (kk) and confidence Pol- (c, (8)
32:
33:
Out-of-Span Caching:
34:
for i (Ecycle;
35:
(f.).) < Tmin_conf then
36:
Cache features for position
37:
end if end for In-Span Parallel Decoding:
38:
39:
40:
Shägh (i [Scycle; Ceyste] A (o(P Thinh
41:
if Snigh K 0 then
+ (k) for all i E Shigh
42:
Aggressive unmasking
43:
else
44:
Unmask Top-k tokens in [Seyetes Coyytee
Fallback refinement
45:
end if
Seycle Ecyole k + k -
Advance the stable region start
47:
end if
49:
end while
50: return (0)
15


Reviews:
[{"review_id": "mQFkBG0TAt", "reviewer": "Reviewer_g1Di", "summary": "This paper addresses the significant inference latency of dLLMs. The authors introduce \"SlowFast Sampling,\" a new dynamic sampling strategy designed to accelerate generation without substantial loss in quality. \n\nThe method is motivated by three empirically-derived \"Golden Principles\": \n(1) The Certainty Principle, which states that high-confidence tokens are likely correct and stable; \n\n(2) The Convergence Principle, noting that token predictions stabilize over diffusion steps; and \n\n(3) The Positional Principle, observing that confident tokens often appear in contiguous blocks. \n\nThe proposed sampler operates in two alternating stages: a slow \"Exploratory Stage\" to cautiously identify stable regions of the sequence, and a fast \"Accelerated Decoding Stage\" that rapidly decodes these stable regions in parallel. The authors conduct extensive experiments on two dLLMs (LLaDA 8B and Dream 7B, both are SOTA dLLMs) across a wide range of benchmarks, demonstrating significant speedups (up to 15.6x, and 34.2x when combined with a caching mechanism) with minimal performance degradation.\n\nI am not opposed to accepting this paper. It tackles a well-defined and highly relevant problem: making parallel-decoding dLLMs practically fast. The paper's core contribution is a framework for thinking about dynamic sampling in this context.", "questions": "1.  Could you please comment on the process for selecting the hyperparameters? Were they found via a systematic search, and how sensitive is the performance to their joint configuration? How well would you expect the chosen values to transfer to a completely different dLLM architecture or a different data domain?\n2.  To improve clarity and reproducibility, would it be possible to provide a pseudocode algorithm for the main SlowFast Sampling loop in the appendix? This would greatly help in understanding the interplay between the slow and fast phases and the various conditions.\n3.  Regarding the Positional Principle: did you consider or experiment with strategies that could identify and decode multiple non-contiguous stable blocks in parallel during the \"fast\" phase? Do you believe this could offer further speedups?\n4.  Can you provide any statistics from your experiments on the dynamic behavior of the sampler? Specifically, what was the average number of cycles (slow-fast pairs) per generation, and what was the average length of the `[scycle, ecycle]` spans?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Significant Performance Improvement:** The paper deliver on its primary promise of acceleration. Speedups of over 10x with minimal accuracy drops are impressive and represent a major practical contribution to the field of efficient dLLMs inference.\n\n- **Well-Motivated Approach:** The grounding of the SlowFast sampler in the three observed principles is a major strength. It shows that the design is not an arbitrary collection of heuristics but a thoughtful response to the underlying behavior of the generative process.\n\n- **Comprehensive Evaluation:** The experiments are thorough. Using two different SOTA dLLM architectures demonstrates the generalizability of the approach. The wide array of benchmarks, spanning reasoning, general knowledge, and code, provides a holistic view of the method's impact.\n\n- **Strong Baseline Comparisons:** The paper includes comparisons not only to vanilla diffusion sampling but also to other accelerated strategies (Fast-dLLM, semi-autoregressive) and, most importantly, to a state-of-the-art autoregressive model (LLaMA3 8B). Showing a throughput advantage over LLaMA3 is a powerful statement.\n\n- **Clarity of Presentation:** The paper is well-written and structured. The figures, particularly the conceptual overview in Figure 1 and the pipeline in Figure 3, are very effective at communicating the core ideas.", "weaknesses": "- **Methodological Complexity and Hyperparameters:** The SlowFast sampling method introduces a non-trivial number of new hyperparameters (`Kmax`, `Whist`, `σ²_stable`, `τ_min_conf`, `τ_high_conf`, etc.). While the authors provide an ablation study (Figure 5 & 6), it raises questions about the method's sensitivity and the effort required to tune it for new models or tasks. The description in Section 3.3 is dense and would benefit greatly from a formal algorithm block (pseudocode) to improve clarity and reproducibility.\n\n- **Oversimplification of the \"Positional Principle\":** The paper observes that confident tokens often cluster (Positional Principle), but the proposed method only acts on a single contiguous block `[scycle, ecycle]` at a time. It's plausible that a model might become confident about multiple, non-contiguous regions simultaneously (e.g., the beginning and end of a sentence). The current method does not seem equipped to exploit this.\n\n- **Limited Analysis of Method Dynamics:** The paper presents strong final results, but provides little insight into the internal dynamics of the SlowFast sampler. For instance, what is the typical length of an accelerated \"fast\" span? How often does the fallback top-k refinement (in the fast phase) get triggered? Such analysis would provide a deeper understanding of *why* the method works so well.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1zlXHQwUUS", "reviewer": "Reviewer_dgDC", "summary": "The paper proposes a decoding algorithm, SlowFast sampling, for dLLMs that alternates between a slower exploratory decoding phase and a fast parallel decoding phase. Evolving around three empirical “golden principles” observed, SlowFast sampling consists of: a slow decoding phase — (1) confidence-based cautious decoding, (2) end point of convergence prediction with confidence thresholding, (3) stability check using variance of candidate tokens as a termination condition; and a fast decoding phase — (1) bidirectional predicted value caching (for out-of-span positions), (2) in-span parallel decoding, (3) fallback top-k refinements.", "questions": "1. When comparing “+cache”, are the authors using dual cache or prefix cache? Please specify the exact cache policies.\n\n2. Could you provide sensitivity analyses for (i) the end-point-of-convergence criterion the parallel-decoding confidence threshold, and (iii) the cautious-decoding confidence threshold? These seem central to both correctness and speed.\n\n3. How transferable is SlowFast to other dLLMs like Dream? Since dLLM is a fast evolving field, do your principles still hold as remasking strategies deviate from LLaDA, or does the method risk becoming obsolete? A discussion on compatibility layers or auto-tuning would be valuable.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. FastSlow decoding consistently reports better speedups than Fast-dLLM at comparable accuracy, indicating the design improves efficiency rather than merely trading off performance.\n2. Clear presentation: the method, the case study and confidence-map visualizations make the two-phase “slow/fast” dynamics easy to understand and help readers see why the method works.", "weaknesses": "1. Overly complex and hyperparameter-heavy. The method introduces many knobs to tune (e.g., stability window/variance, multiple confidence thresholds, top-k settings). The paper only ablates the stability check; ablations for the other choices are missing and necessary.\n\n2. TPS reporting (e.g., Table 4) labels both AR and LLaDA as “1$\\times$” which makes it unclear what the true reference baseline is. Please normalize to one reference (e.g., AR or vanilla LLaDA) and report consistent relative and absolute TPS. As shown, the incremental TPS gains versus AR appear minimal.\n\n3. The paper reads like a bundle of techniques rather than a single, principled mechanism with good motivation. more analysis explaining how much each component drives the gains would strengthen the contribution.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "krXRiqprWi", "reviewer": "Reviewer_REmQ", "summary": "This paper introduces a training-free inference-time acceleration method named SLowFast sampling for masked diffusion models. The approach is inspired by three proposed principles: high-confidence tokens should be preserved, each token tends to converge over iterations, and the decoded tokens admit certain positional bias. The algorithm leverages these ideas by first identifying convergence regions and then refining the regions in parallel. Experiments on LLaDA and dream on several common benchmarks demonstrate that the proposed approach achieves notable speedups without substantial performance drop.", "questions": "1. Could the authors explain why the accuracy on certain benchmarks for the base model and the acceleration algorithms are lower than originally reported? For instance, in original LLaDA paper they report ~37 accuracy on MMLU-pro but the authors report ~23 here in this paper.\n\n2. It would be necessary to include another baseline that naively reduces the number of sampling steps w/o any modification on the sampling algorithm.\n\n3. It is a bit confusing that the speedup reported in table 4 is ~15x, while it is only ~8x in table 2. I understand that they achieve different scores (31 vs 33), but still feel that the discrepancy here is confusing. An ideal way would be to report the curve with two axes being speedup and score, and compare the curves of different approaches.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. This paper tackles an important problem of training-free decoding acceleration for masked diffusion models. More importantly, it helps improve the accuracy-efficiency frontier of masked diffusion models.\n\n2. The experiments cover both LLaDA and Dream on a suite of benchmarks, which is a comprehensive evaluation.", "weaknesses": "1. As the experiment results show, it seems like most of the speedup comes with the help of dLLM-cache. Compared with Fast-dLLM (parallel) in the two scenarios (with or without cache) the gain in speedup is not that substantial.\n\n2. The paper proposes to leverage three principles. However, there is no ablation study that separately investigates the role/contribution of each principle as well as some detailed designs (e.g. top-k refinement) in terms of enhancing the speedup. As a heuristic approach, the proposed method requires more in-depth analyses to help understand how each of the components contribute to the improved efficiency.\n\n3. Some of the presentation should be improved. The paper claims to achieve 15x and up to 34x acceleration in abstract and introduction. However, the paper should be cautious of such claims and I think a less misleading way would be adding the model and dataset that these numbers were obtained from (GPQA in this case). The remarkable speedup might come only in certain datasets where the accuracy is less affected (e.g. the base model already achieves a pretty low accuracy so the negative effect on the accuracy when using acceleration techniques would be marginal).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9qLRtwqYT3", "reviewer": "Reviewer_xDJE", "summary": "This paper presents SlowFast Sampling, a dynamic decoding framework for diffusion-based large language models (dLLMs). Guided by three principles—Certainty, Convergence, and Positional—the method alternates between exploratory and accelerated decoding phases, achieving efficient token generation. Integrated with dLLM-Cache, the method achieves up to 34× speedup with negligible accuracy loss. Experiments on LLaDA and Dream across various benchmarks demonstrate consistent and substantial improvements in throughput and efficiency.", "questions": "+ Could the authors provide a step-wise breakdown of inference time to show which parts of the pipeline (exploration vs. acceleration) contribute most to overall speedup?\n+ Could the authors report tokens-per-second (tps) across different diffusion step counts to make the acceleration claims more transparent and easier to validate?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "+ Proposes a novel and practical dynamic sampling strategy with a clear underlying rationale.\n+ Demonstrates significant acceleration (up to 34×) with minimal loss in accuracy.\n+ Offers conceptual clarity via the three principles, making the approach interpretable.\n+ Shows real-world relevance, outperforming strong autoregressive baselines in throughput.", "weaknesses": "+ Although the results are strong, the paper lacks a detailed analysis of computational efficiency at different diffusion step counts. Providing tps statistics would make the acceleration claims more transparent and easier to validate.\n+ The paper does not discuss any failure cases or corner scenarios. Including such examples would increase trust and reproducibility.\n+ While the integration with dLLM-Cache is effective, its contribution is only reported numerically. A clearer explanation or figure showing how cache reuse interacts with SlowFast stages would enhance interpretability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)