Generation then Reconstruction: Accelerating Masked Autoregressive Models via Two-Stage Sampling
Authors: Feihong Yan
Yao Zhu
Peiru Wang
Pang Kaiyu
Qingyan Wei
Huiqi Li
Linfeng Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: generation then reconstruction
acceleration
masked autoregregrassive model
image synthesis
Primary area: generative models

Abstract: Masked Autoregressive (MAR) models promise better efficiency in visual generation than continuous autoregressive (AR) models for the ability of parallel generation, yet their acceleration potential remains constrained by the modeling complexity of spatially correlated visual tokens in a single step. To address this limitation, we introduce Generation then Reconstruction (GtR), a training-free hierarchical sampling strategy that decomposes generation into two stages: structure generation establishing global semantic scaffolding, followed by detail reconstruction efficiently completing remaining tokens. Assuming that it is more difficult to create an image from scratch than to complement images based on a basic image framework, GtR is designed to achieve acceleration by computing the reconstruction stage quickly while maintaining the generation quality by computing the generation stage slowly. Moreover, observing that tokens on the details of an image often carry more semantic information than tokens in the salient regions, we further propose Frequency-Weighted Token Selection (FTS) to offer more computation budget to tokens on image details, which are localized based on the energy of high frequency information. Extensive experiments on ImageNet class-conditional and text-to-image generation demonstrate 3.72X speedup on MAR-H while maintaining comparable quality (e.g., FID: 1.59, IS: 304.4 vs. original 1.59, 299.1), substantially outperforming existing acceleration methods across various model scales and generation tasks.
Our codes will be released in https://github.com/feihongyan1/GtR.

Published as a conference paper at ICLR 2026
GENERATION THEN RECONSTRUCTION: ACCELERAT- ING MASKED AUTOREGRESSIVE MODELS VIA TWO- STAGE SAMPLING
Feihong Yan 1,2 Linfeng Zhang 22
Yao Zhu
Peiru Wang Pang Kaiyu
Qingyan Wei 2
Huiqi Li m1,4
Beijing Institute of Technology 2 Shanghai Jiao Tong University Zhejiang University Beijing Key Laboratory of Intelligent Diagnosis Technology and Equipment for Optic Nerve- Related Eye Diseases, Beijing, China 23 Corresponding authors huiqililbii edu. cn zhanglinfengés edu.
ABSTRACT
Masked Autoregressive (MAR) models promise better efficiency in visual gen- eration than continuous autoregressive (AR) models for the ability of parallel generation, yet their acceleration potential remains constrained by the model- ing complexity of spatially correlated visual tokens in a single step. To address this limitation, we introduce Generation then Reconstruction (GtR), a training- free hierarchical sampling strategy that decomposes generation into two stages: structure generation establishing global semantic scaffolding, followed by detail reconstruction efficiently completing remaining tokens. Assuming that is more difficult to create image from scratch than to complement images based on a basic image framework, GtR designed to achieve acceleration by comput- ing the reconstruction stage quickly while maintaining the generation quality by computing the generation stage slowly. Moreover, observing that tokens on the details of image often carry more semantic information than tokens in the salient regions, we further propose Frequency-Weise offer more computation budget to tokens image details, which are localized based on the energy of high frequency information. Extensive experiments on Ima- geNet class-conditional and text-to-image generation demonstrate 3.72x speedup on MAR-H while maintaining comparable quality (e.g., FID: 1.59, IS: 304.4 vs. original 1.59, 299.1), substantially outperforming existing acceleration methods across various model scales and generation tasks. Our codes will be released in https://qithut com wwwp////ewweoeo//nnnoeeeeee
Token Selection (FTS)
INTRODUCTION
Motivated by the successes of autoregressive (AR) models in natural language processing, the realm of computer vision has increasingly explored the autoregressive paradigm for visual content generation (Van Den Oord et al. 2016; Chen et al., [020 Yu et al.) [022]] Tian et al.) 2024 Zhao al. 2025a). Early endeavors adopt pixel-by-pxel generation strategies Van den Oord et al. 2016), treat images as flattened sequences, and apply causal modeling directly. However, the autoregressive formulation suffers from severe computational inefficiency, due to the natural inability to the parallel generation. To solve this problem, an alternative direction emerges through next-set prediction, exemplified by Masked Autoregressive (MAR) models (Chang et al., [022]] Li et al. 2023 2024). MARs adopt encoder-dccoder architecture with bidirectional attention, where the encoder produces conditioning vectors z for each token, subsequently guiding a diffusion process to generate the final tokens. This framework enables simultaneous prediction of multiple tokens in a single forward pass while maintaining competitive generation quality.
Although parallelism has been provided by MAR, directly generating too many tokens in a single step usually brings a significant degradation in the quality of generation in practice. Concretely, this problem arises from the inherent complexity of modeling high-dimensional joint distributions. Visual

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Generated
High Freq. Tokens High Freq. Tokers Image (tovel-Space) (Latent-Spaces
Generated Image
High Freq. Tokens High Freq. Tokens (Poel-Spacce)
(Latent-Space)
bifference between Adjscent Two Stepa
Figure 1: The difference of token features in the adjacent steps. Once a token decoded, its adja- cent tokens tend to exhibit significant changes.
Figure 2: Correspondence between high-frequency regions pixel space and feature space. Each triple shows three images: original image, pixel-space high-frequency heatmap via high-pass filtering, and frequency heatmap of MAR's conditioning vectors. The spatial align- ment demonstrates that high-frequency tokens in feature space indicate regions with fine-grained textures and high-frequency details.
tokens exhibit strong spatial correlations that fundamentally violate conditional independence assump- tions, necessitating explicit modeling of interdependencies through joint probability distributions rather than simplified factorizations. When simultaneously predicting multiple tokens, MARs must estimate the joint probability distribution over all target tokens whose modeling difficulty increases with the number of predicted tokens, limiting the speed of parallel generation. To solve this problem, we begin by exploring the intrinsic property of MAR.
The spatially adjacent tokens tend to influence each other. It is well-acknowledged that tokens in the same image region usually share similar semantic information. As result, the spatially adjacent tokens tend to influence each other. Figure shows the difference between tokens in the adjacent steps, which demonstrates that when one token has been decoded, its adjacent tokens tend to be significantly influenced, indicating that adjacent tokens should be decoded separately.
checkboard
LIVAN
contiguous bleck
GIOLL SU
311
23
317
VN
Tolors have bean Decoded Tilone hevn not bean Decoded
Generated images with different random seeds
Figure 3: Comparison of generation consistency in two sampling methods when 50% tokens have been generated and the remaining tokens are generated using different random seeds. Dark blue and light blue indicate the tokens that have been decoded and not decoded, respectively. The top row shows checkerboard pattern that distributes generated tokens (dark blue) uniformly throughout the image, yielding consistent generation results. The bottom row shows contiguous block pattern that concentrates generated tokens the upper region, resulting a diverse generation.
Covering more spatial locations indicates creating more information. Figure 3 compares the generation results of two different sampling orders, including "checkerboard" where the decoded tokens are isolated and spatially uniformly distributed, and "contiguous block". where the tokens in the upper regions are generated. For each sampling order, we first decode the same 50% tokens, and then decode the left 50% tokens with different random seeds. Interestingly, we find that the seven images generated by "checkerboard" are almost identical, while the seven images generated by "contiguous block" exhibit significant difference, indicating that the content of images has been almost fully decided when the generated tokens are spatially uniformly decided. Concretely, decoding the tokens that cover most spatial locations has already "created" the main body of the image, while generating the left tokens is more likely to be an image "reconstruction" which does not bring new information and is much easier than "creation".
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Based on the two observations, we propose GtR (Generation-then-Reeeee Reconstruction) which introduces a two-stage checkerboard-sty.e generation process. In the first stage, we randomly generate tokens in the light blue positions in the checkerboard, which guarantees that the decoded tokens are not closely spatially adjacent and can cover most spatial positions in the images. As a result, this stage "creates" slower speed (e-g., generating
the main semantic content of the image, and thus it is performed fewer tokens at each step). Then, in the second stage, since "reconstruction" is much easier than "creation", GtR introduces highly parallel generation, which decodes all the left tokens in very few steps with a high parallel ratio, which can even be performed by a single step, thus bringing extreme acceleration without loss of generation quality.
Besides, the computation process of MAR includes not only the encoder and decoder, but also a diffusion model which maps the latent of each decoded token into a continuous vector, which also accounts for noticeable computation costs. The original MAR pays the same computation costs for each token, while ignoring the fact that the token with great details and complex patterns is much more difficult to generate. In this paper, we further propose Frequeney- -Wiggtted Token Selection (FTS), a training-free strategy that allocates more diffusion steps to the tokens with more details. As demonstrated in Figure 2, FTS applies a Fourier transformation to the latents of tokens, and then identifies the tokens with larger
energy as the tokens with more details.
RELATED WORK
Next-Token Autoregressive Visual Generation. Autoregressive models have been increasingly adopted across wide range of domains, including natural language processing, visual understand-
ing and generation (Liu et al., 2025b 2026 2025a Zhao et al. 2022). Autoregressive models for visual generation must map two- wwo-dimensional image structures into sequential one-dimensional
token representations. Early studies explored RGB pixel synthesis via row-by-row raster-scan (Chen)
eet al. 2020: Gregor et al., 2014 Van Den Oord al. 2016 Van den Oord et al. 2016) methodol-
ogy. VQGAN (Esser et al. 2021) establishes the foundation by converting two-dimensional visual
content into one-dimensional discrete token sequences, while VQVAE-2 (Razavi al. 2019) and
2022) extend it with hierarchical or stacked representations. Building on this foundation, LlamaGen (Sun et al., 2024) scales the architecture 3B parameters on the LLaMA (Touvron et al.) 2023) framework, achieving quality comparable to competitive diffusion models. Recent advances extend autoregressive generation beyond fixed raster-scan orders. RAR (Yu et al. 2024), DAR (Xu et al. (025), and D-AR (Gao & Shou, 2025) introduce randomized, diagonal, or diffusion -nspiree factorization schemes, while FractalGen (Li et al. (025) expand token repre- sentations through flexible entities and fractal composition, thereby enhancing modeling flexibility and performance. Nevertheloss, next-token- wowen--reeiictionn paradigms face a fundamental computational bottleneck: their strictly sequential nature allows only one token per inference step, and the long token sequences of images make real-time generation prohibitive.
RQ-Transformer (Lee et al.
Next-Scale Visual Generation. Multi-scale generation offers an alternative to mitigate the com- putational cost of token-by-tn by-token prediction. MUSE (Chang et al. 2023) hierarchically generates low-resolution tokens with a base transformer and then SuperRes is used to generate high-resolution ones. Hi-MAR (Zheng et al. 2025b) further uses a unified masked autoregressive model that first predicts resouuluttion token pivots capture global structure, then conditions them generate the full-resolution image. However, it introduces additional KV cache overhead and necessitates expensive, unstable training with specialized multi-scale loss. VAR (Tian et al. 2024) adopts a decoder-only transformer configuration for next-scale prediction, which reduces computational overhead and improves scalability. In addition, E-CAR (Yuan et al.), [024] and NFIG (Huang et al., 2025) adopt a coarse-to-fing strategy by multistage generation in continuous token space and us- ing frequency-aware decomposition respectively. CTF (Guo et al., 2025) mitigates quantization redundancy by autoregressively predicting coarse labels and refining them in parallel. However, this multi-scale tokenization methodology exhibits fundamental incompatibility with the 1D flat token representation paradigm that has been extensively integrated into contemporary multimodal systems, potentially limiting broader applicability.
Next Set-of- Tokens Visual Generation. Non-autoregressive generation methods have emerged as promising alternative to sequential token prediction. MaskGIT (Chang et al. 2022) pioneered the
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next set-of-tokens prediction paradigm, leveraging BERT-style (Devlin et al. 2019 Wei al. 2025
bidirectional attention mechanisms and enabling parallel replacement of multiple masked tokens through stochastic sampling confidence-base based selection strategies. MAR (Li et al. 2024) extends MaskGIT's framework by introducing diffusion-based loss functions to transform from discrete token representations to continuous token spaces, thereby mitigating information loss. Building on this paradigm, ZipAR (He et al. (025a),, NAR (He et al.] (2025b), and Harmon (Wu et al. 2025a) exemplify efforts to relax strict sequential prediction, either through spatially localized or neighboring-toke -orkknnggt parallelism, or by unifying visual understanding and generation within shared masked autoregressive framework. xAR (Ren et al. (222a)) predicts coarse- guirse-rained units (e.g., cell of tokens) per step, enabling parallel intra-unit generation while maintaining inter-unit autoregressive dependencies. Although next set-of-tokens prediction reduces sampling iterations, MAR remains limited in parallelization and requires computation across all token positions, leading to suboptimal efficiency. Recent methods have attempted to accelerate next sct-of-tokens prediction. LazyMAR (Yan al. 2025) accelerates MARs through token and condition caching mechanisms, but it does not improve the sampling strategy of MARs. DISA (Zhao et al. (025b) proposes a diffusion step annealing strategy for the diffusion head in MARs, but it overlooks the differences in modeling difficulty among different image regions. -aakon- (Besnier et al.) 2025) introduces the
Halton scheduler into MaskGIT, but its fixed ordering reduces diversity in image generation.
METHOD
3.1 PRELIMINARY
Image Generation Next-Token Prediction. With an image tokenizer, the input image IE Hx is then encoded into h x w tokens, where = H/p. w = W/p, and denotes the downsampling ratio. These tokens are reshaped into a sequence x = (x², ,"""...,,,""" x") with n h w, arranged raster sean order. The joint distribution factorized as p(x) I P (x' x= 22,22 "" 11-),, where p (xr x tion of token x given previous tokens x!' to =11 However, raster-order prediction cannot capture overall image structure early in generation, and sequential processing scales linearly with resolution.
..........__........... represents the conditional distribu-
Image Generation as Next-Set Prediction. To overcome the sequential bottleneck, next-set predic- tion enables simultaneous generation of multiple tokens within each inference step. Let denote random permutation of [1, 2..... n2.. The joint distribution is decomposed into prediction steps:
N
p(r x²)
II p(xk X2 ..... X k=1
(1)
where X fo ( r3 represents the k-th token subset under permutation т, with constraints UN X = fx x²} and X n'n'' = for i + j. MARs rewrite this formulation in I) via bidirectional attention, then continuous- valued token generation.
two parts: generating conditioning vectors 2² = f(X',.. modeling x | 2² through diffusion, enabling
Limitations of MARs. MARs exhibit two fundamental limitations: (1) Spatial correlation modeling: Random permutation may simultaneously predict spatially adjacent tokens, which is more challenging than predicting spatially separated tokens. (2) Violation of romposition-to-dee
paradigm: Humans typically perceive and create visual content hierarchically, first establishing global structure then refining local details. However, random token sampling violates this paradigm and may create blank areas in later generation stages, degrading quality due to insufficient context.
3.2 GENERATION THEN RECONSTRUCTION
To address the limitations of MARs, we propose GtR waweatiinnnteee-eoontttuuuu introduces a two-stage checkerboard-style generation process that decomposes visual generation into semantic creation followed by detail reconstruction. As illustrated in Figure 4 given an image tokenized into h x tokens, let i, represent the row and column indices of each token position, the generation stage randomly generates tokens where (i + j) mod 2 = 0 at a slower speed (e.g.. generating fewer tokens at each masked autoregressive step) to establish the main semantic structure,
which
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timestep
timestep
timestep
timestep f+1
1
tokens have decoded
Step
tokens have not decoded
tokens decoded in this step
GTR (Generation Stage)
GtR
Raster
Random
(Reconstruction Stage)
Figure 4: Comparison of different token sampling strategies. The proposed GtR formulates the generation process as a two-stage checkerboard procedure: generation stage establishes global seman- tic structure through spatially non-adjacent tokens conservative speed, followed by reconstruction stage completing remaining tokens within 1-2 steps through highly parallel generation.
while the reconstruction stage subsequently generates the remaining tokens where (i+ mod 2 = in very few steps with a high parallel ratio, which can even be performed by single step to bring extreme acceleration without loss of generation quality.
Algorithm Stage Partitioning
Require: resolution x w, number of stages K, initial stack T and token set R = {(i,j) :
h, 0 S < w)
1: for k == 1 to K do
2:
Uy f [(, R () mod 2 2*
Extract tokens with remainder 2
3: Push onto stack T
4: R (- E R (i + mod 2 = 0}
Update remaining tokens with remainder
5: end for
6: Push onto stack T
7: (Sx) <- Pop all elements from stack T in LIFO order
8: return stage partitions {
However, the simple two-stage framework may suffer from delayed semantic structure establishment, as randomly sampling tokens within the generation stage could concentrate generated tokens in localized regions, thereby postponing the formation of global semantic guidance until later steps. To address this limitation, we further subdivide the generation stage into K sub-stages, which enables the first sub-stage generate spatially uniform tokens distributed across the entire image in fewer masked autoregressive steps, thereby establishing the fundamental semantic structure as soon possible and providing robust conditioning for subsequent generation.
Algorithm| partitions the complete token set into K disjoint subsets S = S2. Sk J. which allocated to the K 1 sub-stages of the generation stage and the reconstruction stage, where UK_ SR {x ..... x") and S; n, = 0 for # j. Algorithm literatively bisects the unassigned token set R. into two subsets at each iteration: one subset is allocated to a new sub-stage, while the other becomes the updated R for the subsequent iteration. This hierarchical decomposition ensures that tokens within each subset uniformly distributed throughout the image, effectively increasing the spatial distance between simultaneously predicted tokens and reducing their interdependence. As more tokens generated, the fundamental semantic strueture of the image becomes increasingly established, providing stronger conditioning for subsequent token prediction and enabling later stages k to achieve higher generation rates rt--
Tokens generated later stages are conditioned on the tokens from all previous stages. A causal dependency is formed between these stages. Consequently, the joint distribution of all tokens be reformulated as follows:
,(x!,...,,'")
x.) = II pSSa (5....,S-I)
(2)
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where p(S, ,,,...,St-1) represents the conditional distribution of tokens in stage k given all tokens generated in the previous stages S₁ through Sk-1- After initial structure generation, the checkerboard pattern ensures that each ungenerated token is surrounded by generated tokens. This forms strong causal dependencies where ungenerated tokens are directly conditioned on their neighboring generated tokens, which constrains the token distributions and enables the remaining half of image tokens to be generated within as few as to 2 masked autoregressive steps.
Intra-Stage Masked Generation. Within each stage we generate all tokens Sy through Ma masked autoregressive steps, where M& I |S At each masked autoregressive step m within stage we use next-set prediction sample subset of tokens X S Sk, conditioning on all tokens from previous stages and the tokens already generated within the current stage. Formally, the probability of generating tokens stage ht is decomposed as follows:
p(S Sca)
II p( m=1
|
Xk.m- Sek)
(3)
where Skk = - S; represents all tokens generated in previous stages, and Unle Xk.m Sz with X X = 0 for j. The likelihood of the complete token sequence = (x² , , x. can be reformulated follows:
M p(X* k.m
p(
II m=1
[
k.1
Xk.m- 1.mm-1 18ck)
(4)
Equation 4 can be viewed as a reformulation Equation Our method still follows the next- set prediction paradigm, differing only in sampling order. Because MARs are trained on random permutations of all possible token orders, including the sampling order of GtR, our method can be applied to any MAR models in waining-frree manner.
Stage-Aware Diffusion Scheduling. The computational process of MARs includes not only the encoder and decoder, but also diffusion model for modeling the per-token probability distribution. However, traditional MARs apply the same diffusion steps to each masked autoregressive step, ignoring the changes in modeling complexity across different masked autoregressive steps. As first sub-stage of the generation stage establishes fundamental semantic structure through spatially distributed tokens, subsequent generation is guided by more accumulated conditional information and becomes easier. Therefore, we implement linearly decreasing diffusion steps from Taax to Tiin during the generation stage and the diffusion steps Tes throughout the reconstruction stage.
3.3 FREQUENCY- WEIGHTED TOKEN SELECTION
During the reconstruction stage, tokens exhibit heterogeneous prediction complexity, and tokens corresponding to regions with complex and fine textures are difficult accurately model with Tice diffusion steps. To address this limitation, we propose that identifies structurally critical tokens and allocates additional diffusion steps accordingly.
-wreehhee Token Selection (FTS)
Let z' E R denote the conditioning feature produced by the autoregressive model for token x', where D represents the feature dimensionality. To analyze the frequency characteristics of these conditioning features, we apply the Discrete Fourier Transform to each token's 's conditioning vector:
0, 1, ... [D/2] where z' (d) denotes the d-th element of the conditioning feature. The amplitude spectrum is computed from the real and imaginary
F (2') (n) E.D (d) e 2nnd
components of the Fourier transform: A (z') (n) R22 (J (z') (n)) 12 (F (z') (n)) 1/2 where R(-) and I(-) represent the real and imaginary parts of the complex Fourier coefficients, respectively.
The importance score for each token is computed through weighted integration of its frequency spectrum, where higher frequency components receive linearly increasing weights:
D/2] Σ
(z) (n) (1+ [D/2]
(5)
We rank all tokens by their importance scores s' and assign Tatmil diffusion steps to the top 3 high- frequency tokens during the reconstruction stage model complex texture regions more accurately.

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GtR 3.7
61R x 5.5
25% steps x 2.4
12% steps 3.1
Figure 5: Qualitative comparison between Generation then Reconstruction (GtR) and the accelera- tion achieved through step reduction on MAR. GtR enables extreme acceleration while maintaining generation quality, whereas step reduction results in significant visual degradation.
Original
6tR
x
Figure 6: Qualitative comparison generation results between GtR and the original LightGen. GtR achieved a 3.3x speedup while maintaining generation quality comparable to the original LightGen.
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Implementation Details. We evaluate our method on two generation tasks: (1) class-condittonal ImageNet generation using MAR (Li et al., (024) variants (MAR-B/L/H with 208M/479M/943M
parameters) at 256 256 resolution, where images are tokenized into 256 tokens via KL- to- kenizer (Rombach et al. 2022) with 100 diffusion steps following LazyMAR (Yan et al. 2025); (2) text-to-image generation using 7B LightGen (Wu et al. 2025b) at 512 x 512 resolution, where images decomposed into 1024 tokens via VAE encoder with 50 diffusion steps. Text prompts are encoded through T5-XXL (Raffel et al. 2020). For GtR implementation, we use = stages for MAR with generation rates F [2.67, 10.67, 64) and K 4 stages for LightGen with Th = {16, 42.6, 85.3, 256}. Stage-aware diffusion scheduling employs linearly decreasing steps from max 50 to Tiinn 20 during generation stages, with Tiee 20 during reconstruction. FTS
allocates Tesail 50 diffusion steps to the top 3 10% high-frequency tokens.
Evaluation Metrics. For MAR, we generate 50,000 images across the 1,000 classes of ImageNet- IK and evaluate image quality using FID (Heusel et al. 2017) and IS (Salimans et al., 2016) as standard metrics. We measure computational efficiency through FLOPs, CPU latency, and GPU latency. For LightGen, we use GenEval (Ghosh al.) 2023 to evaluate image generation quality.
4.2 CLASS-CONDITIONAL IMAGE GENERATION
We evaluate our method by comparing with the original MAR model, other MAR acceleration methods (Yan al.) 2025 Zhao al. 2025b Besnier al. 2025). and state-of-the-ann image
generation models (Zheng et al.] 2025a] Besnier et al. 2025 Sun et al. 2024) Ren et al., 20256 Li
et al. 2023 Peebles & Xie 2023 As shown in Table it can be observed that: Compared to other
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Table Model comparison results on ImageNet 256 256 class-conditional generation. "MAR-B, -L. -H" denote MAR's base, large, and huge models. "64, 16" represent the number of decoding steps.
Inference Efficiency
Generation Quality
Method
Latency(GPU/s)]
CCUsn)y FLOPs(T)
Speed
Param
FID
IS
MAGE
1.60
12.60
4.19
1.00
307M
6,93
195.8
LDM-4
5.35
27.25
69.50
1,00
400M
3.60
247.7
DiT-XL/2
4.84
196.88
114.38
1.00
675M
2.27
278.2
LlamaGen-3B
1.65
1524.63
3.1B
3.05
222.3
Halton-MaskGIT
0.46
20.13
10.72
1.00
705M
4.17
263.0
Hita-2B
2B
2.59
281.9
FlowAR-H
0.51
60.27
38.43
1.00
1.98
2.65
296.5
MAR-B (64)
0.26
27.83
14.49
1,00
208M
2.32
281.1
+Step=16
0.11 0.15
12.16 67
6.22 8.27
2.33
208M
4.10
247.5 33.6
+Halton
0.11-0.15
12.16
6,22
2.33
208M
3.37 +1.00
257.1 22.00
+DiSA
0.09 0.09-0.17
8.78 19.05
4.82
3.01
208M
2.52 +0.20
272.9
+LazyMAR
0.09-0.17
8.19-19.61
4.11-10.55
3.53
208M
2.64
276.0
+GIR (Ours)
0.07-0.119
7.18.-20.6
10.62
208M
2.37 40.00
253.5
+Step=8
0.08-0.18
8.051119.18
4.84
2.99
208M
13.12 13.122.10.80
180.9 100.2
*Halton
0.08--0.16
8.65
4.84
2.99
208M
9.44
204.6
+DISA
0.06
6.52
3.46 3.46-11.08
4.19
208M
3.62 3.62.41.9
255.7 -2..--5644
+AazyMAR
0.06 0.20
5.499.22.84 22.04
2.70 11.79
5.37
208M
4.37 12.05
241.9 30.2
+GIR (Ours)
0.05-6.21
4.30-23.03
-12.20
6.33
208M
2.76+0.44
274.6
MAR-L (64)
55.62
+Step=16
2.29
331.70
19.33
2.44
(2.00
247.4 48.7
*Halton
0.19 0.29
21.92 83.70
13.42
19.33
2.44
479M
3:24 11.42
261.1 33.0
+DISA
0.16
18.39
11.03
2.97
479M
2.23
281.1-15.0
281.1
10.00
+LazyMAR
0.16 0.33
17.01 61
23.40
3,50
479M
2.11 10.29
284.4
11.7
+GIR (Ours)
0.13
14.98
8.85
3.71
479M
1.81 -0.01
297.4..1
+Step=8
0.14 0.34
15.87
10.21-22.54 22.54
3.21
479M
16.11 114.29
165.0 131.11
+Halton
0.14-0.51
15.87
10.21-22.54
3.21
479M
14.16
155.4_ 140.7
+DiSA
0.12 0.33
13.19
2.85-244.0
4.17
479M
3. 3.86 4.80442.04
254.5 254.55-44.6
MAR
0.11-0.37
10.81_41.81
5.77 26.98
42.25
247.9 18.2
+GIR (Ours)
0.08
8.75
5.18 27.67
6.32
479M
2.33 40.61
281.5
MAR-H (64)
0.81
104.66
64.52
1.00
943M
1.59
299.1
16
0.33-..46
43.39
27.11
2.38
943M
4,49
242.9
+Halton
0.33
43.39
27.11
37.41
2.38
943M
3.18
261.7 17
+DiSA
0.27 -0.44
33.72 77.94
70.94
21.59
42.98
2.99
943M
2.11
283.
16.0
+LazyMAR
0.27 00.54
32.10 (000-772..0 77.05
44..67
3.42
1.94 4..4
284.11 15.0
+GIR (Ours)
0.22 0.39
27.93 70.73
17.34
47.18
3.72
943M
1.59 10.00
301.4
+Step=8
0.26
31.52 77-34
20.88 43.66
3.09
943M
17.66
158.0
+Halton
0.26
31.52
20.88
3.09
943M
11,85 10..26
191.2
+DiSA
0.19 0.19-p.02
24.10-80 56
15.44_49.0s
4.18
943M
3.15.1.56
365.5-331.4
+LizyMAR
0.18--0.6
21,28
12.74-51.28
5.06
4.06.42.47
249.3
+GIR (Ours)
0.15-0.66
19.98
10.27
10.27
6.28
943M
2.16
285.6_13.5
state-of-the-art image generation models, MAR-H + GtR maintains the lowest GPU latency while achieving the best generation results. (2) Compared with the original MAR, our method achieves a 3.72x speedup while maintaining nearly identical generation quality. Additionally, both MAR-H + GtR and MAR-L + GtR simultaneously surpass the original MAR's s smaller variants in both quality and efficiency. (3) Our method outperforms other MAR acceleration methods (HaltonMAR, DiSA, and LazyMAR) in both speedup and generation quality. Even at extreme acceleration ratios, while other MAR acceleration methods exhibit significant degradation in generation quality, method maintains comparable visual fidelity as demonstrated in Figure
4.3 TEXT-TO-IMAGE GENERATION
We evaluate the acceleration performance of GenEval at 512 x 512 resolution and compare it with the original LightGen (Wu et al. 2025b) and other text-to-image Generation models (Rombach et al.) 2022 Podell et al. 2024). As shown in Table 2 GtR achieves higher acceleration ratios compared to the original model and LazyMAR, while simultaneously delivering superior generation quality. Figure fillusttttss the results under a 3.3x acceleration setting with GtR, where the generated images remain largely consistent with the original outputs.
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Table Performance comparison in 512 512 on GenEval. GtR achieves 3.82x speedup while maintaining superior generation quality compared to baseline LightGen.
Methods
Inference Efficiency
Generation Quality
LatencyrGPUkk)
Speed
Param]
Single Ohj-1
Two Obj.1
Colors"
Countingt
Position:
Color Attri,
Overall"
Stv1.5
0.98
0.77
0.37
0.05
0.42
SDXL
1.46
1.00
2,08
0.63
0.23
0.51
0.12
0.04
0.05
0.26
Llamagen
3.13
1.00
0.78
0.19
0.16
0.10
0.03
0.09
0.01
0.10
1 Lighttion, 32
1.03
1.00
3.48
0.99
0.60
0.83
0.39
0.15
0.33
0.55
+Step=16
0.25-0.25
348
4.00
0.85.
pouaz
6.15
0.30-
0.50
4LaryMAR
-082
-0.0
0.82 4003
0,40,
um
+Gtk (Ours)
0.31-0..1
3.32
3.4B
0.50
0.58 5.80
0.86:4.03
0.41,0.02
0.14-6.03
0.35-
0.50.
sStep=12
0.68
1.52
3.4B
4.90
0.59
0.88 0.88...05
0.36
0.12
0.25
0.53
*AaryMAR
0.43
2.40
3.4B
0.98
0.56 0.56-2.00 -0.04
0 0.85.
0.37
0.18
0:25
0.53
+GBR (Churs
0.27--0.5 0225
1.00
0.60
0.84
0.30,0.00
0.14 .p.c
0.36.
Table 3: Ablation studies for GtR and FTS effectiveness on class-conditional generation. GIR* applies GtR to MAR's encoder-decoder, GtR applies GtR to MAR's diffusion.
Latency (GPU/s)
Lateney (CPU/s)
FLOPs (T)
GtR*
GIR
FTS
Speed
FID
IS1
X
0.59 0.22
76.05
28.61
45.13
19.30
1.43
1.64 10.05
297.3 1.8
0.29 0.52
35.27
69.39
22.19
42.33
2.90
1.70 00.11
300. 1
X
X
0.43-o.38
53.58
55.08
33.92
30.60
1.90
1.59.0.00
300, 300.441.3
0.22 0.59
27.02
77.64
17.28
47.24
3.73
1.65 00.06
303.4 44.3
0.22-0.59
27.93
17.34- 47.18
3.72
1.5940.00
304.445.3
Table 4: Ablation studies of figh-frequency pivot token selection methods Frequency-Weigee Token Selection (FTS). Evalua- tion on ImageNet 256x256 during the reconstruction stage.
Table 5: Ablation studies of four token sampling strategies.
Method
FID
ISt
Method
FLOPs(T),
Speedt
FID
18
Raster
24.61
120.6
Origin
64.52
1.00
1.59
299.1
Subsample
5.19
247.4
+Random
17.34 17.14
47.18
3.72
1.64
304.5.5.4
+Low-Freq.
47.18
3.72
1.64
301.5+2.4
Random
1.82
288.8
-Fhull-Enhaneed
18.04 46.48
3.58
1.65
301.6,225
+High-Freq. (Ours)
17.34
47.18
47.18
3.72
1.59.0.00
304.445.3
GtR (Ours)
1.59
304.4
4.4 ABLATION STUDY
Effectiveness of GtR and FTS. Table shows the ablation study of the proposed GtR and FTS. It observed that: (1) When applied individually to either the encoder-decoder or diffusion components of MAR, GtR consistently delivers significant computational gains while maintaining generation quality. (2) When GIR is applied simultaneously to both the encoder-decoder and diffusion, we achieve 3.73x speedup with only a marginal increase in FID of 0.06 compared to the original MAR. (3) When FTS is further applied, the best results are achieved.
Token Selection Strategies. As shown in Table during the reconstruction stage: Random: random token selection; Full-Enhanced: apply enhanced diffusion steps to all tokens in the reconstruction stage; High-Freq.: top 10% tokens with highest importance scores 8"; Low-Freq. top 10% tokens with lowest importance scores sf. Full- -allneedd and Low-Freg. both underperform Random, indicating that low-frequency tokens are unsuitable for enhanced diffusion sampling. High-Freq. applies enhanced diffusion steps to fine-grained detail tokens, enabling the capture of complex local patterns and textural nuances.
evaluate four token selection strategies
Impact of Sampling Order. Table shows the ablation study of different token sampling orders using MAR-H: Raster (top-left to bottom-right), Subsample (4-quadrant raster), Random (permuta- tion), and GtR spatially adjacent. Subsample outperforms Raster by establishing global structure through quadrant distribution but still suffers from adjacent token prediction within quadrants. Random achieves better
Raster performs worst because predicted tokens

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Published as a conference paper at ICLR 2026
performance by mitigating spatial adjacency but may create large blank regions in later generation stages, while GrR systematically addresses these limitations for optimal performance.
CONCLUSION
In this paper, we introduced Generation then Reconstruction (GtR), training-free free hicrarchical sampling strategy that significantly accelerates MARs by decomposing generation into structure creation and detail reconstruction stages. By exploiting the observation that spatially adjacent tokens tend to influence each other and that "reconstruction" is considerably easier than "creation", GtR brings significant acceleration without loss of generation quality. We further proposed Frequency- Weighted Token Selection (FTS) to allocate computational resources based token complexity. Through comprehensive experiments on ImageNet and text-to-image generation, we demonstrated 3.72x acceleration while maintaining comparable quality, establishing practical framework for efficient parallel visual generation that advances the applicability of MARs in real-world scenarios.
ACKNOWLEDGEMENT
This research was supported by the Shanghai Science and Technology Program (Grant No. 257R 1402278) and CCF- Baidu Open Fund.
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A
APPENDIX
A.1
USE OF LLMS
During the preparation of this manuscript, we used Large Language Models (LLMs) as auxiliary tools for language editing and presentation enhancement. These tools did not contribute the research conception, methodology, experimental design, data analysis, or any scientific conclusions presented in this work.
Language editing. We used LLMs to assist with grammatical corrections and sentence clarity improvements. LLM suggestions were carefully reviewed, and we made all final decisions regarding text modifications.
Presentation formatting. LLMs provided suggestions for table and figure formatting to enhance visual presentation. All scientific content and data remained unchanged.
Notation consistency. LLMs occasionally assisted with ensuring consistency in mathematical notation and formatting throughout the manuseript.
We take full responsibility for all content in this manuscript. The research contributions, experimental intellectual work. LLMs served solely as editing
work, and scientific analysis are entirely assistance tools and had no involvement in the research process or content generation.
A.2 ABLATION STUDY ON HYPERPARAMETERS AND ROBUSTNESS
35
Oriia
30
X0
02
M 05
Speed
Speed
130
1b
101
(0)
Figure 7: Sensitivity analysis for FTS per- centile 3 on MAR-H at 32 steps on ImageNet 256x256. (a) FID vs. B. (b) IS vs. 3. Perfor- mance remains stable when 3 ranges from 0.1 0.5, but degrades when 3 exceeds 0.5.
Figure 8: FID vs. Speed trade-off curves on MAR-H at 32 steps on ImageNet 256x256. (a) Varying initial diffusion steps Tmax- (b) Varying generation stage steps with reconstruction stage fixed 1 or 2 steps.
We conduct comprehensive ablation studies to evaluate the sensitivity of GtR to key hyperparameters and demonstrate its robustness across different configurations.
FTS percentile 3. Figure presents the sensitivity analysis for the FTS percentile on MAR-H 32 steps. Performance remains relatively stable when /3 ranges from 0.1 to 0.5, with minimal FID fluctuations. However, when exceeds 0.5, generation quality begins degrade. This behavior indicates that allocating enhanced diffusion steps to too many tokens becomes suboptimal, as most tokens in the reconstruction stage benefit from strong conditioning information from surrounding generated tokens and do not require additional modeling capacity. Based on these results, we set 0.1 the default value, ensuring enhanced diffusion steps are allocated only to tokens with complex high-frequency content.
Trade-off curves for varying configurations. Figure presents comprehensive trade-off analyses. Figure 8
a) shows the FID vs. Speed curves when varying initial diffusion steps T GtR out- performs the original MAR for both Tmax = 50 and Tmax 100 settings across all speedup ratios. Figure b)) presents the trade-off when varying generation stage steps while fixing the reconstruction stage to either or steps. Regardless of the reconstruction stage configuration, GtR consistently outperforms the original MAR. Notably, when the speedup ratio exceeds 3.2x, the original MAR exhibits severe quality degradation, whereas GtR maintains stable generation quality even at 8x speedup, validating the robustness of our two-stage design.

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Published as a conference paper at ICLR 2026
Figure 9: Impact of sampling steps MAR generation quality on ImageNet 256x256. (a) FID vs. Sampling Steps. (b) IS Sampling
138
211
OuR Origtal
Steps. GtR consistently achieves superior gener-
ation quality compared to original MAR across
170
all sampling steps.
10
Sampinng saps
Sampingatep strps
(3)
[6]
Impact of sampling steps. Figure shows the FID and IS curves as a function of sampling steps for both the original MAR and GtR on ImageNet 256 256. GtR consistently achieves superior generation quality compared to the original MAR across all sampling steps, with the advantage becoming more pronounced original MAR cxhibits severe quality degradation, GtR maintains stable performance, demonstrating the effectiveness of our checkerboard partition strategy in preserving generation quality under extreme acceleration settings.
the number of steps decreases. At very low step counts where the
Table 6: Ablation study on checkerboard pattern selection for MAR-H at 32 steps on ImageNet x56x256.
Pattern
FID
ISt
(i + 1) mod 2 = 0 1.59
304.4
()+)) mod 2 1
1.60
302.8
Checkerboard pattern selection. We evaluate the impact of checkerboard pattern selection by comparing the two complementary partitions: (i++)) mod 2 versus ++ j) mod 1. As shown in Table 6 both patterns yield comparable generation quality MAR-H at 32 steps, with marginal differences FID (1.59 vs 1.60) and IS (304.4 vs 302.8). This validates the robustness of GtR's checkerboard reconstruction strategy regardless of which specific pattern is selected, as both patterns maintain equivalent spatial separation properties that are critical for minimizing token dependencies during generation.
These ablation studies demonstrate that GtR maintains stable performance across reasonable hyper- parameter ranges, enabling straightforward application to different MAR models without extensive hyperparameter tuning.
2


Reviews:
[{"review_id": "GLCxjHCAEk", "reviewer": "Reviewer_tfUA", "summary": "The authors identify some issues with MAR and related frameworks, particularly related to modeling potentially correlated tokens as independent when denoising them parallely. On this hypothesis, they suggest a two step generation methodology - first generate tokens in a checkerboard like pattern so as to avoid adjacent tokens being unmasked parallely, and once this provides sufficient context, reconstruct the rest of the tokens quickly in 1-2 steps, conditioned on the previously generated tokens. This leads to inference time accelerations as suggested by the empirical results, with minimal or no degradation.", "questions": "Please see the weaknesses section above", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The authors present a comprehensive review of the literature in the context of visual generation, and identify shortcomings, proposing a sound method to address these limitations\n- The two stage generation pipeline is a fairly novel contribution, utilizing ideas from previous works on token ordering\n- The inference time speedups in terms of real time latency is promising\n- The authors perform a detailed set of ablations, explaining their design choices", "weaknesses": "- The key hypothesis of this method is that the checkerboard-like pattern would lead to good global context and therefore more coherent generation. However, it is possible for images to have structures that extend to larger regions of the image, thus still potentially producing inconsistencies in generation. A deeper discussion on this point would be helpful\n- Adding on to the previous point, since the checkerboard pattern followed by reconstruction is meant to produce more coherent generations, this approach should have led to generations with better fidelity, alongside latency speedups. However, the empirical results suggest neutral quality and only latency speedups when compared to corresponding baselines. It would be helpful to see the generations with highest FID to inspect and analyse the shortcomings of the proposed method", "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": "Ff7JqiSkqW", "reviewer": "Reviewer_fAqu", "summary": "This paper proposed a two-stage sampling strategy (Generation then Reconstruction, GtR) for MAR. The goal is to sample images with fewer autoregressive steps while maintaining comparable quality. Specifically, the generation stage samples image tokens in a checkerboard sequence, using more steps to establish global semantic scaffolding and structure. The reconstruction stage then samples the remaining tokens with fewer steps to fill in the details. Additionally, the paper introduces a Frequency-Weighted Token Selection strategy for choosing diffusion steps. Experimental results demonstrate that this GtR strategy can accelerate sampling speed.", "questions": "Please refer to weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The proposed sampling strategy is effective and intuitively easy to understand.\n\n2. The paper is clearly written.\n\n3. The performance in ImageNet-256x256 is impressive.", "weaknesses": "1. Limited Generalizability: The method's generalizability is a significant concern. While it performs well on ImageNet, its performance drops considerably on the T2I task (as shown in Table 2). This suggests that the proposed sampling strategy might be an overfitted, heavily-tuned solution for the ImageNet dataset and may not generalize well to other datasets or tasks. Moreover, the proposed strategy's applicability seems limited, as it is designed specifically for the MAR.\n\n2. The novelty of the work is limited. The core concept of establishing a global structure before refining details (\"global-to-detail\") has been explored in prior work like MUSE[1] and Hi-MAR[2] (missing reference and comparison). The paper's main contribution appears to be adapting this concept specifically to the sampling process of MAR.\n\n3. Insufficient Experiments: The paper only reports results for a very sparse set of sampling steps (e.g., steps 8 and 16 for C2I; steps 12 and 16 for T2I). It is unclear why step 8 was omitted for the T2I task, and the performance at other step counts remains unknown. The authors should provide the curves (e.g., FID vs. Sampling Steps) to show the full performance trend. Moreover, there are no trade-off curves to analyze the FID vs. speed when: (a) varying the step of the generation and reconstruction stages, (b) varying the autoregressive steps and the diffusion steps.\n\n4. Do not provide an introduction to the baseline methods (Halton, DiSA, LazyMAR).\n\nI am open to raising my score if the authors can address these concerns in their rebuttal.\n\n[1]Chang, Huiwen, et al. \"Muse: Text-to-image generation via masked generative transformers.\" ICML 2023.\n\n[2]Zheng, Guangting, et al. \"Hierarchical Masked Autoregressive Models with Low-Resolution Token Pivots.\" ICML 2025.", "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": "jJCiO3P1EO", "reviewer": "Reviewer_FhEK", "summary": "This paper proposes Generation-then-Reconstruction (GtR), a training-free, two-stage sampling strategy to accelerate Masked Autoregressive (MAR) visual generation. GtR first generates a spatially non-adjacent “skeleton” of tokens (checkerboard / conservative generation stage) to establish global semantic structure, then reconstructs the remaining tokens in very few highly-parallel steps. The authors also introduce Frequency-Weighted Token Selection (FTS) to allocate extra diffusion steps to tokens with high high-frequency energy (i.e., fine details). On ImageNet class-conditional and text-to-image setups the paper reports ~3.72× speedup for MAR-H with comparable FID/IS. The method is presented as training-free and applicable to existing MAR pipelines.", "questions": "- The paper report “3.72× speedup”, but it is not clear whether this is measured as (a) total wall-clock on standard hardware, (b) encoder FLOPs only, (c) decoder/diffusion FLOPs, or (d) a mix. A breakdown (encoder vs diffusion vs IO) and experiments on multiple hardware targets would increase reproducibility and confidence.\n- I recommend conducting a Hyperparameter sensitivity study, including experiments that sweep K, Trec, β (FTS percentile), and Tdetail to show how sensitive quality and speed are. A small table/heatmap showing FID vs speed for several settings would help practitioners choose defaults.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Simple, practical idea with strong engineering value.** Splitting generation into “creation” (structure) and “reconstruction” (detail) is intuitive and requires no model retraining, which makes it immediately usable in deployed MAR systems.\n- **Clear empirical speed/quality benefit.** The reported ≈3.72× acceleration on MAR-H while maintaining generation quality is compelling evidence of practical value.\n- **Ablations and comparisons.** The paper contains ablations across sampling orders and token selection strategies (Raster/Subsample/Random/GtR and High-Freq/Random/Full-Enhanced), which strengthens the empirical claims.", "weaknesses": "- **Limited analysis of failure modes and generality.** The paper demonstrates gains on ImageNet and a text→image generator, but it is unclear how GtR behaves with different tokenizers (VQ variants, continuous latents), non-square layouts, or with very detail-dense images where global structure is weak. The paper makes the empirical claim that reconstruction is “much easier than creation” but does not deeply analyze where this breaks down.\n- **Limited theoretical justification for checkerboard schedule.** The checkerboard / stage partition is intuitively motivated (reduce adjacent simultaneous predictions) but lacks theoretical analysis or simple metrics demonstrating why it is near-optimal vs. other structured partitions. The ablations show GtR > Random, but more insight would help adoption.", "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": "kwkzHZbgWC", "reviewer": "Reviewer_ZCm1", "summary": "Based on the two observations: (1) the spatially adjacent tokens tend to influence each other; and (2) covering more spatial locations indicates creating more information, this paper proposes Generation-then-Reconstruction (GtR), a novel sampling strategy to accelerate image generation in Masked Autoregressive (MAR) models, which decomposes generation into two stages: structure generation establishing global semantic scaffolding, followed by detail reconstruction efficiently completing remaining tokens. It also proposes Frequency Weighted Token Selection (FTS) to offer more computation budget to tokens on image details. Extensive experiments on ImageNet class-conditional generation and text-to-image synthesis demonstrate that GtR achieves significant speedups while maintaining competitive or superior image quality.", "questions": "1.\tThe paper uses K=3 for MAR and K=4 for LightGen. Is this choice empirical or theoretically motivated? How does K affect the quality-speed trade-off?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe \"generation-then-reconstruction\" paradigm is conceptually novel, which is a training-free hierarchical sampling strategy and expands the mainstream random/sequential generation mode.\n2.\tThe proposed Frequency-Weighted Token Selection (FTS), a training-free strategy that could allocate more diffusion steps to the tokens with more details.\n3.\tCompared with other accelerated model, the proposed method achieves faster inference speedup for MAR while maintaining comparable quality, which better balance between generation speed and generation quality.", "weaknesses": "1.\tSome of the organization and arguments in the paper are problematic: a). In line 013, the MAR is the Masked Autoregressive (MAR) models from [1] , but it is not clear whether AR specifically refers to a discrete autoregressive model or a continuous autoregressive model. If it is discrete autoregressive model, the inference efficiency of MAR is lower than AR; b). In line 134, the xAR should not be classified as Next-token autoregressive visual generation, because more than one token is generated at each step.\n2.\tAlthough the specific division stages and the average generation rates for each stage are described in section 4.1, the number of inference steps of each stage is unclear. And it is not clear why it is set up like this.\n3.\tThe generated tokens in the generation stage satisfied the condition (i+j) mod 2 = 0 in all experiments. Lack the ablation study about setting the condition (i+j) mod 2 = 1. In addition, lack the ablation study about β.\n4.\tInconsistent writing format in section 3.1. “Image Generation as Next-Token Prediction.” and “Image generation as next-set prediction” .\n\n[1] Autoregressive image generation without vector quantization.", "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)