Plug-and-Play Context Feature Reuse for Efficient Masked Generation
Authors: Xuejie Liu
Anji Liu
Guy Van den Broeck
Yitao Liang
Venue: NeurIPS 2025 poster ()
Keywords: Non-autoregressive Image Generation; Masked Generative Modeling; Inference Acceleration;
Primary area: deep_learning

Abstract: Masked generative models (MGMs) have emerged as a powerful framework for image synthesis, combining parallel decoding with strong bidirectional context modeling. However, generating high-quality samples typically requires many iterative decoding steps, resulting in high inference costs. A straightforward way to speed up generation is by decoding more tokens in each step, thereby reducing the total number of steps. However, when many tokens are decoded simultaneously, the model can only estimate the univariate marginal distributions independently, failing to capture the dependency among them. As a result, reducing the number of steps significantly compromises generation fidelity. In this work, we introduce ReCAP (Reused Context-Aware Prediction), a plug-and-play module that accelerates inference in MGMs by constructing low-cost steps via reusing feature embeddings from previously decoded context tokens. ReCAP interleaves standard full evaluations with lightweight steps that cache and reuse context features, substantially reducing computation while preserving the benefits of fine-grained, iterative generation. We demonstrate its effectiveness on top of three representative MGMs (MaskGIT, MAGE, and MAR), including both discrete and continuous token spaces and covering diverse architectural designs. In particular, on ImageNet256 class-conditional generation, ReCAP achieves up to 2.4$\times$ faster inference than the base model with minimal performance drop, and consistently delivers better efficiency–fidelity trade-offs under various generation settings.

Plug-and-Play Context Feature Reuse for Efficient Masked Generation
Xuejie Liu Anji Liu? Guy Van den Broeck Yitao Liang' Institute for Artificial Intelligence, Peking University School of Computing, National University of Singapore Computer Science Department, University of California, Los Angeles Schooo of Intelligence Science and Technology, Peking University xjli mjliuêstu pku. edu. cn, amjiliuScomp. nus mus.edu.a.. edu.sg guyvdbêcs ucla. edu, yitaol@pku edu.
Abstract
Masked generative models (MGMs) have emerged as a powerful framework for image synthesis, combining parallel decoding with strong bidirectional context modeling. However, generating high-quality samples typically requires many iter- ative decoding steps. resulting in high inference costs. A straightforward way speed up generation is by decoding more tokens in each step, thereby reducing the total number of steps. However, when many tokens decoded simultaneously, the model can only estimate the univariate marginal distributions independently. failing capture the dependency among them. As result, reducing the number of steps significantly compromises generation fidelity. In this work, we introduce ReCAP (Reused Context-A ware Prediction), a plug-and-plae module that accelerates infer- ence in MGMs by constructing low-cost steps via reusing feature embeddings from previously decoded context tokens. ReCAP interleaves standard full evaluations with lightweight steps that cache and reuse context features, substantially reducing computation while preserving the benefits fine-gratie ane-grained, iterative generation. We demonstrate its effectiveness
top of three representative MGMs (MaskGIT [5], MAGE (27. and MAR (29). including both discrete and continuous token spaces and covering diverse architectural designs. In particular, on ImageNet256 [7] clnsscooniiional generation, ReCAP achieves up to 2.4x faster inference than the base model with minimal performance drop, and consistently delivers better efficiency-fidelty trade-offs under various generation settings. Our code is publicly available at htthhtt::/// /gg teigt.h....e. com www.co/./miiiee http:///leeeeeeejieeeaaeee/ee/ae RReCAP
1 Introduction
The remarkable success of sequence modeling in language generation its adoption in image modeling, where transformer-
41
42 has inspired
models [5T
g
17 learn the joint
distribution over sequences of visual tokens using either autoregressive
10
48
22
44
40] strategies. Among them, Masked Generative Models 56 have emerged particularly compelling framework, achieving competitive generation quality while supporting efficient parallel decoding. The advantages both performance and efficiency have positioned MGMs as promising alternative to latent diffusion for high-resolution image generation, with recent work also extending their success
or non-autoregressive [[]
58
(MGMs) 27
29 54
52
models 45 39 2
text-to-image synthesis 4 [2
Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure 1: FID vs. inference time on ImageNet256 class-conditional generation. As the number of decoding steps increases, MAR [29] achieves better FID but incurs high inference cost. ReCAP significantly acceler- ates MAR by replacing part of full-eval steps with low-cost steps, achieving 2.4x x faster in- ference for MLAR-Huge with minimal quality loss (FID 1.56 vs. 1.57). For fair compari- son, we adopt the version of REPA without interval guidance (2], as also reported in the original paper [57]. denotes the use of KV caching 47 for fast inference.
2.6
2.4
VAR-520"
L-VIT-H
DIT-XL
2.2
REPA
MAR-L- RcCAPIO
2.0
1.8
0.0
0.5
1.0
1.5
2.0
2.5
Inference Time (sec/ / image)
Despite these promising results, MGMs still face limitations in inference efficiency. As illustrated in Figure 1 applying MAR [29] state-of-the-ard MGM, to class-condittonall image generation on ImageNe1256 7 reveals a consistent trade-off: higher sample quality requires more decoding steps, which results in longer inference time. This limitation becomes even more pronounced in large-scale models, where each additional step incurs substantial computational overhead.
We attribute this limitation to a fundamental trade-off in the MGM decoding paradigm. To reduce the total number of generation steps, MGMs decode multiple visual tokens at each step. Ideally, the model should sample from the joint distribution over all tokens being decoded. However, it can only predict the univariate marginal distributions for each token independently due to the sequence-to- -o-quueneee nature of Transformers. Therefore, while reducing the number of decoding steps is computationally appealing, often results significant degradation in generation quality. To address this dilemma, we pursue an orthogonal direction: lowering the computational cost per decoding step while retaining the advantages of fine-grat fine-grained iterative updates.
Our approach is motivated by a key empirical finding: when only a small number of tokens are updated between decoding steps, the Transformer feature embeddings of the previously decoded context tokens remain largely unchanged. This property is particularly beneficial in settings with many decoding steps, where each step only decodes a small subset of tokens. We leverage this insight to design lightweight decoding steps that reuse the feature representations of context tokens computed during earlier steps. As illustrated in Figure replacing a portion of the full evaluations in the decoding procedure of MAR (29 with these lightweight steps allows us substantially reduce inference time with minimum reduction on generation quality.
In summary, we propose ReCAP (Reused Context-A ware Prediction), a simple yet effective plug- and-play module for accelerating MGM inference. ReCAP interleaves standard full evaluations with cheaper partial evaluations that reuse cached attention features for the unchanged context tokens. We empirically demonstrate that ReCAP consistently improves quality-efficiency trade-offs across variety of MGMs, including discrete MGMs (MaskGIT [5), MAGE (27)) and also MGMs with continuous-ve -valued tokens (MAR (29), covering different architectural designs and evaluation setups. Notably, on ImageNet256 class-conditional generation, ReCAP accelerates inference for MAR MAR-Huge Huge by 2.4x, while preserving its state-of-the-art FID with no architectural edits or additional training.
2 Related Work
Masked Generative Models (MGMs) originate from non-autoregressive sequence modeling in machine translation [31, 14], offering parallel decoding and faster inference than autoregressive models. Recently, MGMs have been successfully adapted to image generation. As a pioneering work. MaskGIT [5] demonstrated competitive performance on ImageNet using as few as 8 decoding steps, achieving better -fffiiiyeffcciincc trade-off's than diffusion models. Several works aim improve MGM generation quality: Token-Critie [25] introduces auxiliary model for guided sampling, MAGE [27] unifies representation and generation via varied masking ratios, and AutoNAT [36] and AdaNAT [37 search for better sampling schedules through optimization-bl based strategy or reinforcement learning algorithm. However, these methods incur significant cost at longer steps. To bridge the gap with SoTA continuous diffusion models, MAR [29] extends the MGM framework to continuous-valuee token spaces, mitigating the information loss from discrete tokenization, and achieves state-of-Ne ofate-of-the-art FID below 2.0 on ImageNet.

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Inference Efficiency in Generative Models. Accelerating inference is a key challenge in generative modeling. In autoregressive models, techniques such as key-value (KV) caching [51 47 and speeu- lative decoding [24] reduce redundant computation. In diffusion models, efficiency has improved through advanced solvers [3 34,, classifier-free guidance [19] and guidance interval sampling [21] While early MGMs benefit from fewer decoding steps and relatively efficient inference [5], achieving state-of-the-rr performance remains costly. For example, MAR [29] requires 256 decoding steps to match the quality of leading diffusion models [57 [6] resulting in significant inference overhead.
3 Preliminaries of MGMs
In this framework, raw image X is first encoded into sequence of N visual tokens X = { using a pretrained tokenizer. Most approaches leverage vector-quantizal (VQ) autoencoders [50]
I 43 tokenizers, which map image patches to indices in a learned codebook, representing each token X; as a categorical variable A transformer-based sequence model is then employed to learn
the joint distribution over (X)) x=)==1
To model the sequence data X, MGMs adopt a masked prediction objective, learning to predict
masked tokens conditioned on a subset of variables termed the context. This training objective inspired by masked language modeling tasks used in models like BERT g and discrete
diffusion models [I] The model minimizes the following cross-entropy loss:
[xx) = - -Er~g (aaraa)))aaaaa)11
log (
where r is sampled masking ratio and g- is a masking distribution that replaces r-fraction of tokens in æ by [MASK]. At test time, the model begins with an empty context (i.e., all tokens are masked) and gradually decodes chosen subset of tokens, expanding the context at each step by incorporating the newly decoded tokens. Notably, each step updates multiple tokens in parallel, allowing high-quality generation with significantly fewer decoding steps.
Specifically, the decoding procedure begins with fully masked sequence x At each step t E (1....,T), the model predicts token values based on the current sequence (t-11) We define ny as the total number of decoded tokens after step and îi = n4 R4-1 the number of tokens to be decoded at t. Let M₁ = {i x (t-1) = [MASK]] denote the masked positions at the beginning of the
t-th step. The sampler selects subset S MM of size fg either uniformly [2 or proportionally to
the model's predicted confidence [7] (see Appendix C). For each E Sc, the model samples x? from the trained MGM po (X, (X,|x((t11)
4 The Inference Challenge of MGMs
MGMs offer a powerful framework for high-quality image generation by progressively refining predictions through iterative masked sampling- However, shown in Figure [ achieving high- fidelity results typically requires a large number of decoding steps, leading to significant inference costs. For example, MAR-Large (29]. state-of-the-att MGM, achieves an FID of 1.8 using 128 decoding steps, but its performance deteriorates sharply to an FID 15.9 when constrained to only 8 steps (measured using the official code). For even larger models such MAR- inference latency can exceed 2 seconds per image when using hundreds of steps, posing a challenge for the practical deployment of MGMs in latency- senesiysee applications.
We attribute this undesirable reliance on numerous decoding steps to the inherent limitations of its parallel decoding procedure. As illustrated in Section 3 in each step I,, multiple masked tokens are sampled independently from the conditional distribution p(z, (t-1)). neglecting intricate depen- dencies among simultaneously unmasked tokens. Similar issues have been identified in discrete diffusion models for masked language modeling (2] where multiple refinement steps are needed to recover coherent outputs due to parallel yet independent updates.
2 Although we describe our method in the context of discrete MGMs, our method is directly applicable to MGMs with continuous-valus tokens (Appendix D
3

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As result, reducing the number of decoding steps inherently limits the model's ability to capture inter-token dependencies, thereby degrading generation quality, Using large number of steps and allowing more incremental and context-aware updates helps alleviate this issue, but at the cost of significant computational overhead. Specifically, unlike causal transformers, which support efficient reuse of intermediate attention states via key-value caching [47], the bidirectional nature of MGMs necessitates recomputing the attention-based features over all N tokens in the sequence at each step, which incurs ((N2) inference cost in each update.
These challenges can be summarized in one central question in our paper: can we reduce the per-step computation cost while preserving generation quality We answer the question in its affirmative by showing that we can cache and reuse feature embeddings of previously decoded tokens with minimum performance drop with the so-called cheap update steps. By balancing the regular and cheap steps, our method achieves substantially better trade-off between inference speed and generation fidelity.
5 Inference Scaling via Context Feature Reuse
To mitigate the inference inefficiency of MGMs, we aim to construct computationally lightweight decoding steps that accurately simulate standard many-step MGM decoding at significantly lower cost. Specifically, we interleave the original 7 full evaluation steps with T low-cost steps, thereby increasing the number of decoding steps without increasing computational burden proportionally. This allows the model to better capture inter-token dependencies, which leads to better fieel-fi trade-offs. Importantly, our method requires no change the model architecture and introduces no additional training cost, making it simple plug-in mechanism applicable to a broad range of existing MGM frameworks.
In each decoding step, the model has to re- compute Transformer feature embeddings for all tokens in the sequence, even though only a small subset of these tokens are actually modi- fied/unmasked between consecutive steps. This necessary because the applied bidirectional attention mechanism allows every token's 's rep- resentation to depend on all others; thus, even a small input change can, in principle, propa- gate globally and alter all token embeddings. However, we hypothesize that when only a few tokens are newly updated, the feature embed- dings of the previously decoded context tokens change only slightly, reducing the need for fre- quent recomputation.
K=2 K-4
K=8
K=24
K=48
K=96
K-16
-
K-32 - K-64
K-128
Similarity of Context Features
1.0
0.9
0.8
0.7
0.6
0.5
0.4
2
2
2
Number of Updated Tokens
Figure 2: Context feature stability during itera- tive decoding. We measure similarity between con- text representations before and after token updates, using a pretrained MaskGIT 50K ImageNet256 samples. At each decoding stage, we extract the input embeddings to the attention module for the K already-decoue pooled within each layer to obtain an aggregated context vector. Cosine similarity is computed be- tween these vectors before and after updates and averaged across layers; shaded regions indicate layer-wise standard deviation. Greater stability at larger K supports reusing cached features in later decoding stages.
To validate this hypothesis, we analyze the representations computed by a pretrained MaskGIT [S] model on 50K samples from the ImageNet256 validation set. For each sample, we randomly select K tokens as context and mask the remaining ones, simulating an inter- mediate decoding state with K already-decoded tokens. As shown in Figure 2 each curve corre- sponds to a different value of K, and the x-axis denotes the number of masked tokens subse- quently unmasked.
tokens. These are average-
To quantify how the context features evolve dur- ing decoding, we incrementally unmask/update growing number of masked tokens, corre-
sponding to increasing positions along the x-axis, and measure how the feature embeddings of the K given tokens change as a result. Specifically, we extract the input embeddings to the attention module (i.e., pre-QKV projection features), verage-pool them across the K context tokens each layer, and compute the cosine similarity between these aggregated features before and after each

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step #1
Context tokens
Mosked
Decodedd forget tokers
Masked target tokens
Full-FE (1,0)
Local-FE (t:t)
Local-FE (t:2)
x²=
step #1
Pr( x
Pr( (xtr))
KV
KV
KV
step #T
KV Cache
Reuse
Grouped Decoding Pipeline
Recomputed QKV
Coched KV
Concatenation
Figure 3: Grouped Decoding Pipeline with Cached Attention. Inference is organized into T groups, each performing one Full-FE and several Local-FE steps. the Full-FE, full attention is computed over the entire sequence, and KVs for the static context tokens and other masked tokens are cached. In each Local-FE, only the QK of the target tokens are recomputed while the cached KVs are reused to form the full attention context. The context feature reuse mechanism effectively reduces computation cost in local evaluation steps.
update (y-axis). We repeat this process for multiple values of K E 42,4,8,... 128) to simulate decoding states at various stages.
The results in Figure provide strong empirical support for our hypothesis. Across all values of K, when only a small number of tokens are updated (left side of the x-axis), the cosine similarity remains close to 1. indicating that the representations of previously decoded tokens change minimally. This suggests that attention features for the decoded context can be safely eached and reused in subsequent steps, enabling more efficient computation.
Moreover, we can observe in Figure 2 that as decoding progresses and the context becomes richer with K increases, the extent of feature drift further diminishes, suggesting that the internal representations associated with context tokens become increasingly stable. This empirical evidence suggests that cached context embeddings can be increasingly reused in later decoding steps, with minimal fidelity loss introduced. Building on these observations, we propose a grouped decoding strategy that interleaves full and partial function evaluations to enable context feature reuse and improve inference efficiency. The overall pipeline illustrated in Figure 3] Intuitively, the key idea the Transformer feature embeddings of unchanged tokens. We implement this by caching and reusing their corresponding key-value (KV) pairs attention computation, as detailed in the following.
cache and reuse
Grouped Decoding Pipeline. As illustrated on the left of Figure 3, we organize the MGM inference process into T grouped decoding stages, where masked tokens
are progressively converted into
decoded context tokens over time. The detailed structure of each grouped step / shown in the central gray box of Figure 3 Within each group I, a target set of masked tokens S, is selected and decoded using multiple light-weight steps. These target tokens are marked in red and are initially masked As decoding progresses, they are gradually replaced with decoded tokens
Each group consists of Full Function Evaluation (Full-FF) at sub-step (1, 0), followed by I, Local Function Evaluation (Local-FE) sub-steps (t, 1), (t, (2). At each sub-step j E [0, 4), disjoint
5

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subset 5 C S₁ is decoded and used as context in later sub-steps. At the end of group 4, all tokens in St will be unmasked, serving as the input context for the next group t + 1.
Full-FE with Cache Construction. At sub-step (t, 0), i.c., the "Full-FE" panel of Figure 3 we perform a full attention computation over the current sequence (t-11) This includes computing QKV representations for all tokens. We then cache the KVs for the complement set St, which consists of the context tokens
and unselected masked tokens (..). These cached KVs (highlighted in ) denoted as S, kucced cached will be reused in subsequent Local-FEs. Next, we decode the first subset
5!") by sampling from the model distribution p(X; (t)+))),, producing the updated sequence (r(10)
Local-FE with Reused KVs. In each Local-FE sub-step (t,j) for j E [1, 44], we decode the subset 5! based the current sequence (t)..-1), Instead of recomputing attention for all tokens, only recompute the QKVs for the target subset 5 (highlighted in These are then concatenated with the cached KVs to form the full attention context:
Isst KTNN vdk
attn s( = Softmax
VI:N+
Concat(v s(o) . (acched) dg denotes the dimensionality of the key/value vectors. Each Local-FE then produces x: (f-5) by sampling from the model distribution conditioned on (t---1), This process continues until all 4 Local-FEs are completed. While the initial Full-FE incurs a full (N22) cost, each subsequent Local-FE performs a much cheaper update with complexity Oi N). where âu 5 < N. Group then concludes by setting (*)) := x (t.44)
keached) VI:N where k1:N- Concat(k sfn, &g,
Efficiency and Fidelity Trade-off. Overall, our method realizes a (T + -sstep generation process, where T" =-= 4 denotes the number of inserted Local-FEs. This strategy effectively adapts the KV caching mechanism to the bidirectional masked generation. Unlike autoregressive transform- where each decoding step inherently mache-friendl due to causal masking- masking--bidrectioons bidirectional MGMs require periodic full evaluations to prevent error accumulation. Our grouped decoding frame- work interleaves exact (Full-FE) and approximate (Local-FE) steps, offering flexible trade-off between inference speed and generation fidelity.
6
Experiment
Our method ReCAP (Reused Context-A ware Prediction) is plug-and-play approach that can be seamlessly integrated into the inference pipeline of existing MGMs. By interleaving full and partial attention computations, ReCAP significantly reduces the per-step inference cost. In this section, evaluate whether the use of Local-FEs can effectively lead to efficiency gains and, more importantly, whether it can achieve better trade-off's between generation quality and inference speed.
To this end, we apply ReCAP to three representative MGM baselines and conduct a thorough evaluation. Our experiments span diverse set of settings, varying in task (class-conditional vs. unconditional generation) and model architecture (decoder-only vs. encoder-decoder). The selected baselines are: i) MaskGIT [5): A widely-used discrete MGM that uses VQGAN tokenizer [II], followed by a Transformer trained with a BERT-style masked modeling objective, as described in Section ii) MAR [29]: A state-of-the-ar continuous-valuee MGM designed to avoid quantization artifacts by operating on latent embeddings. It reconstructs masked tokens via a per-token diffusion loss. (see Appendix D) iii) MAGE (27): A discrete MGM improving MaskGIT by incorporating variable mask ratio training objective, which serves as a strong unconditional generation bascline that does not rely pretrained self-supervised features [28 38)
Among these, MaskGIT uses an decoder-only architecture, while MAR and MAGE adopt an encoder- decoder architecture following the Masked Autoencoders (MAE) [17] As demonstrated in the following sections, ReCAP is model-agnostic and can effectively applied across diverse model designs. We report Fréchet Inception Distance (FID) [18] and Inception Score (IS) 46) following common practice 9 All inference times are re-evaluated using the official implementations on single NVIDIA A800 GPU with default batch size of 200 and reported as time per image.

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Table 1: Performance of MaskGIT w/ and w/o ReCAP on ImageNei256 class-conditional gen-
eration w/o CFG [9] # Steps = + denotes the total number of decoding iterations. u the
number of initial grouped steps without Local-FEs when applying ReCAP. Results show that ReCAP reliably reduces inference time while maintaining competitive FID.
MaskGIT-r (Full only)
MaskGIT-r+ ReCAP
# Steps
FIDI
Time]
# Full-FE(T)
# Local-FFE(T)
FIDJ
Time.
16
4.46
0.095
0
8
5.02
0.055
8
12
4.50
0.076
20
4.18
0.118
10
15
5
4.23
0.094
24
4.09
0.142
10
16
8
4.09
0.107
32
3.97
0.189
12
22
10
3.98
0.137
6.1
Improving MaskGIT with ReCAP
MaskGIT adopts a cosine decoding schedule and a confidence- token sampler. When adapting ReCAP, the same token sampler obtain S, after each Full-FE step (see Appendix BB)
Following the decoding schedule used in MaskGIT, early decoding steps reveal only small number of tokens, while later steps decode progressively more. Recall in Figure we show that context features become more stable as more tokens are decoded. Therefore, we introduce Local-FEs primarily in the later grouped steps. Specifically, for MasAGIT+RcCAP. let T denote the number of grouped decoding steps, which also corresponds to the number of Full-FEs (recall from Figure (ouure3jttr that each group begins with Full-FE). We define ie., 11m = 0. After step u, we insert one Local-FE per step, i.e., 1a+1 = 1, where T is the total number of Local-FEs and the total number of decoding steps equals T +T'
the number of initial steps that only perform Full-FE,,
To assess the impact of ReCAP, we conduct a controlled experiment by fixing the total number of decoding steps + T" and adjusting the allocation between Full- and Local-FEs for ReCAP via the parameter u. As base- line, we replace all Local-FEs with Full-FEs, denoted MaskGIT-r (Full only), which serves as a principal "up- per bound" in performance but incurs higher inference cost. Here, MaskGIT-r represents MaskGIT with an enhanced sampling schedule, demon- strating improved inference scaling with increasing de- coding steps compared to the original MaskGIT in Fig- ure 4 (see Appendix A). The FIDs and the corresponding runtimes per image of both methods are presented in Ta- ble [ In each row using certain number of total steps, GaTKGITT++ReCAP achieves notable inference speedups by replacing subset of Full-FEs with cheaper Local-FEs. For example, with 32 total steps, ReCAP reduces inference time from 0.189s 0.137s per image while maintaining nearly identical FID (3.97 vs. 3.98). With 20 steps, although the FID slightly increases from 4.18 4.23, the inference time drops to match that of the 16-step baseline -wheli while significantly outper- forming it (FID 4.46). These results demonstrate that ReCAP effectively improves quality-speed trade-offs the base MaskGIT, achieving comparable or better performance at lower cost. For more detailed ablation study of the hyperparameters, e.g., T, T', and 1,, please refer to Appendix F.4
7.0
MaskarT-r
6.5
MaskaIT
6.0
MAR-B
5.5
U-VITH
5.0
re-implemented
4.5-
4.0
0.05
0.08
0.1
0.15
0.2
Inference Time (sec/ image)
Figure 4: FID inference time for MaskGIT variants and comparative mod-
els. * taken from the MaskGIT pa-
per 15]. I: with CFG [19]. U-ViT 2
adopts 7 sampling steps in this figure.
We further visualize this improvement in Figure comparing ReCAP against various strong baselines. The "MaskGIT-r (Full only)" and "MaskGGT--+ReCAA" in Table correspond to MaskGIT-r and Ours Figure respectively. By substantially reducing inference time while preserving generation quality, ReCAP enhances the base model's 's inference-scaling behavior. Moreover, without relying on classifier-free free guidance (CFG) [19], our ReCAP-augment quality-efficiene trade-off compared to advanced continuous diffusion models such as U-ViT
model achieves a more favorable
Ht 2 which incorporate both DPM solvers 33 34 and CFG. Our performance is also comparable to AutoNAT (36). which improves sampling via extensive hyperparameter search. However, AutoNAT does not generalize well to longer decoding schedules. In contrast, ReCAP broadly applicable long the performance of the base model improves increase the number of steps.
7

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2.1
2.4
ReCAP(O))
MAR-L-ReCMN (eCuP(
2.0
MAR-H
VAR-d3 ARR-330
REPA
VAR-d24"
REPA"
FID
VAR-d300
DIT XL
1.8
U-VIT-H
1.7
REPA
1.8
0.050.2 0.4 0.6
1.0
0.0
0.5
1.0
1.5
2.0
2.5
Inference Time (sec/ image)
Inference Time (sec /mage)
Figure 5: Speed/Performance trade-off for MAR variants and SoTA baselines. ReCAP consistently improves inference efficiency MaR-Large and -Huee. VARs 488 are SoTA AR models performing next-scale prediction, denotes the use of KV caching [47]. REPA [57]. SoTA flow-matching
model relying on vision foundation models [38], $ denotes the use of advanced guidance interval
sampling 21
DPM solvers 33
augment DiT 39) and U-VIT 2
We provide a comprehensive comparison against more strong generative baselines in Appendix E. such as Token- [25] and DPC (26) with learnable guidance, StralT employing hierarchical modeling [40]. Notably, our MaskGIT-r baseline- obtained by simply adjusting the sampling schedule and increasing decoding aops-alrradyy outperforms these approaches with more complex architectures or guidance mechanisms. ReCAP further improves MaskGlTT-r by offering pld-aay efficiency gains, requiring additional training architectural modifications.
6.2 ReCAP for Continuous-Vallll Valued MGMs
We further adapt ReCAP to the state-of-the-art comtinuous-valued MGMs, MAR [29] Unlike MaskGIT, MAR adopts a MAE-style [77 encoder-decoder architecture, where the encoder operates only on unmasked tokens, while the decoder process the full sequence. Both components employ bidirectional full attention. To fully improve efficiency, we incorporate ReCAP into both the encoder and decoder of MAR- Large and MAR-Huge. For sampling, MAR uses random sampler for token selection, and additionally requires denoising MI.P process for token reconstruction) Following Section 6.1 we set 1ts. = and lu+1u47 1 with u = 2 configurations, such the CFG scale, all follow the official MAR codebase.
T+T' by default. Other sampling
As shown in Figure []] MAR-L and MAR-H require many decoding steps to achieve state-of- the-art FID, resulting in considerable inference cost. Augmenting with ReCAP offers substantial apeedup-achieve up to 2~2.44 faster inference while maintaining the performance (++0.01 FID) to their original counterparts. Furthermore, MAR+ReCAP matches the hest performance of REPA (57), which is obtained by adopting the advanced guidance interval sampling (1 Notably, REPA is leading mowtmiiching model that leverages self-supervised features from vision foundation models (38] for training. While autoregressive models like VAR [48) remain more efficient due to the use of KV caching 47 MAR+ReCAP outperforms them in generation quality.
We further benchmark our method against a wide range of srute-of-the-add generative models un- der classifier-fres guidance, shown in Figure 5 Our ReCAP- augmented variants demonstrate substantial improvements in inference efficiency. For instance, HAR-H with (96+32) de- coding steps, i.e., 96 Full-FEs and 32 Local-FFss, achieves a FID of 1.57 original MAR-H 256 steps (FID 1 while reducing inference time from 2.4s to 1.0s per image. Likewise, MAR-L+ReCAL with (64+20) steps achieves FIID of 1.80 in just 0.33s, outperforming the baseline MAR-L (FID 1.83 at 64 steps) while incurring only an additional 0.02s of inference time from 20 local evaluations. These results highlight the ald--and-play effectiveness of ReCAP in accelerating inference for continuous-valued masked models, enabling strong efficiency-quality trade-offs even when using classifier-free guidance. To further validate ReCAP's generality, we
matching the
'In MAR. inference cost stems from both transformer attention and the per-token diffusion MLP. A detailed cost breakdown is provided in Appendix E.2
8

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Table 2: Benchmarking with state-of-the-art models ImageNet256 class-conditte generation with classifier- frreffee--freeeeee guidance. We compare ReCAP against representative diffusion baselines such as U-ViT[2,, (3), and REPA[57], each evaluated under varying sampling steps to illustrate their step-scaling behavior. Notably, to achieve a FID of 1.8, the original MAR-L requires ~0.66 per image, whereas our MAR-L+ReCAP only needs 0.33s, outperforming the SOTA REPA (0.35s).
denotes the use of interval guidance 21
Method
Time
Method
Params
NER
FID
18
Time (S))
Diffusion Models
VARs
ADM- G 0
554M
250x
4.59
186.7
GIVT-Camsal
VDM++ 20
2B
2.12
267.7
VAR-120
48
608M
10x2
2.57
302.6
LDM-4-
VAR-424
48
1B
10x2
2.09
312.9
0.03
U-VIT-22
501M
50x2
2.29
263.9
0:31
VAR-430
48
2B
10x2
323.1
0.04
25x2
2.64
262.9
0.22
MGMs
7x2
4,03
234.5
0.14
MAR-L 29
479M
256xx2
1.76
294.2
1.20
DITXI
675M
250x2
2.27
276.2
1.71
128xx
1.79
294.2
0.60
150x2
2.39
271
1.03
292.7
50x2
3.75
243.5
0.35
20x2
3.12
276.7
0.14
16
561M
2
276.5
MAR-H 30
943M
256xx
1.56
301.6
2.40
MDTV2-XL-2
676M
250x
1.58
314.7
128x2
1.59
300.1
1.20
CansalFision-H X
1B
250x2
1.64
292.5
Flow Matching Models
Ours
35
MAR-LAR:CAP
479M
(72-24)x2
1.77
293.9
0.37
REPA
57
675M
250x
1.80
284.0
1.76
(64-201x2
293.9
REPA
52
675M
250x 1,4
1.42
305.7
1,50
(20+81x2
2.41
274.8
0.145
MAR-H-
943M
(96-32)x2
1.57
300.6
1.00
6.x
1.80
291.2
0.35
(36-121x2
1.69
291.9
0.40
applied it to text-to-ine -xtttto-imaee (T2I) model at higher $12x512 resolution
53 Detailed results
presented in Appendix E.5
6.3 MAGE with ReCAP for Unconditional Generation
We further evaluate ReCAP on MAGE 27 a state-of-
the-art MGM for unconditional generation without con- ditioning on self-supervised representations
MAGE+ ReCAP(Ours)
(28). MAGE operates on discrete visual tokens using a confidence- based sampling strategy similar to MaskGIT, but adopts an recoder-decoder architecture akin to MAR. Accord- ingly, we apply ReCAP in the same manner as in previous experiments. Specifically, we set u 0, meaning that each grouped decoding step consists of a Full-FE followed immediately by a Local-FF.
MAGE
8.5
8.0
7.3
0.3
0.5
0.8
1.0
1.5
(sec/ Image)
The original MAGE paper only reports performance at 20 decoding steps with FID=9.1, already surpassing prior unconditional models (see Appendix [.3). As shown in Figure 6 extending the number of decoding steps to 128 leads to significant FID improvements (down to 7.12), but also incurs substantial inference cost (0.25s + 1.5s per image). After incorporating ReCAP, we observe a clear improvement in efficiency scaling: inference time is significantly reduced across all steps with negligible performance loss. Detailed FID/IS/imm values and sampling configurations are provided in Appendix [F.3] These results align well with our findings Section 6.1 and Section 6.2 which further highlight the general applicability of ReCAP.
Figure 6: FID vs. inference time for un- conditional generation ImageNet256. ReCAP consistently achieves lower in- ference cost across decoding steps, while matching or improving FID.
Limitation and Discussion
Our work presents ReCAP, a plug-and-pla and-play module designed to accelerate MGM inference. When plugged into a base MGM, ReCAP effectively amplifies the model's inference scaling capability, achieving stronger generation quality at reduced cost. However, this also implies that ReCAP's effectiveness depends on the base model already exhibiting meaningful improvements via scaling decoding steps. Moreover, its assumption of stable context features holds best in high-step regimes, making it more beneficial for large models long sequenceeee generation tasks.

---

Furthermore, our core idea is to construct low-cost steps for non-autoregrresive (NAR) masked generation, with ReCAP serving as simple yet effective instantiation. This opens several promising directions for future work. One is to make the insertion of cheap partial evaluations more adaptive and informed, potentially by some learning strategies. Another is to explore principled ways of combining the outputs from full and partial evaluations to further close the performance gap. More broadly, the concept constructing low-cost steps could be extended beyond attention reuse.
Finally, we note that ReCAP is a general framework for accelerating NAR sequence models, While this paper focuses on image generation, we envision extending ReCAP to other domains, such as language modeling, protein and molecule generation, and beyond.
Acknowledgements. This work was supported in part by the National Science and Technology Major 14902); the DARPA ANSR, CODORD, and SAFRON programs under awards FA8750-23-2-0004 HR001 2590088, and HR001 1 1253014;; NSF grant IIS 151443641; the National University of Singapore under its Start-up Grant (Award No: Research, Cisco Research, and Amazon; and grant from the CCF Baidu Open Fund. Approved for public release; distribution is unlimited.
Project (2022ZD01
gifts from Adobe
10

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Supplementary Material
Implementation Details of MaskGIT-r
First, adopt the pretrained MaskGIT from [36]. with the Transformer architecture following U-ViT [ (25 layers, 768 embedding dimensions).
As reported in the original MaskGIT paper [5], performance does not improve consistently when using more decoding steps, but instead peaks at a "sweet spot" (typically 8-12 steps) before deteriorating. We observe the same trend in Figure Souure/Jun under the default sampling configuration:
Constant sampling temperature T - 1.0 (no temperature scaling)
Choice temperature (2 initialized at 72 = 4.5 with linear decay
Unmasking schedule following cosine function:
n2 = cos
E {0, -1
(I)
2T
where T' the total generation steps and L is the sequence length. The definitions of temperature parameters and 72 are detailed in Appendix [
The original paper S hypothesizes that such sweet spots exist because excessive iterations may discourage the model from retaining less confident predictions, thereby reducing token diversity. We observe that the lack of performance improvement with more decoding steps stems from suboptimal sampling schedules, which obscure the scaling trend. To address the diversity issue, we propose the following modifications for longer decoding steps (16, 20, 24, 32):
Increased initial choice temperature to 72(1) 5.5 (still with linear decay)
Temperature scaling for token sampling (7i (t)):
(t) = Tlo + (1 - 51.0 - TTow)
(2)
where Thigh = 1.0 and Now takes values 0.65/0.68/0.7210..7 for 16/20/24/32 steps respec-
tively.
Polynomial unmasking schedule (replacing cosine):
n = (1 (1- 2-5)
As demonstrated in Figure 4 our revised sampling schedule in MaskGIT-r partiall mitigates the diversity issue, enabling consistent improvements from 12 32 steps.
B Implementation Details of ReCAP
When applying ReCAP both MaskGIT-r and MAGE, maintain the original confidence-basee sampling approach where token selection occurs after each full forward pass (Full-FE). Since confidence scores c(t) logp(X, 2; ((-1)) depend realized token values, first sample all masked tokens X, p(- ((-1x() (with fixed temperature
1.0) before selecting subset S, using the choice temperature schedule from MaskGIT-r (see Appendix [ sampling details).
The target subsets {s} 1 are then constructed by: (1) sorting S. by descending confidence, (2) partitioning sequentially according the polynomial unmasking schedule in Equation 3 and (3) sampling each S using MaskGIT-r's sampling temperature schedule, i.c.. Equation This preserves the original scheduling behavior while adapting to ReCAP's grouped decoding framework.
C Confidence-based Token Sampler
Unlike random selection, the confidence-based token sampler prioritizes tokens based on their confidence scores. Since confidence depends on token values x; the method first performs parallel
14

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Table 3: System-level comparison ImageNet256 conditional generation w/o CFG. Our enhanced baseline, MaskGIT-r, achieves competitive performance compared to more complex approaches. When augmented with ReCAP, MaskGIT-r+ReCAP achieves comparable better FID with reduced runtime, offering plug-and-play efficiency boost. Step counts for ReCAP-enhanced models are reported 7*7", indicating the number of Full-FEs and Local-FEss, respectively.
Method
#Params
NFE
FID 4
IS 1
Time per image (ms)..
ARs
VQGAN
1.4B
256
15.78
74.3
RQTran.
22
3.8B
7.55
134.0
ViTVQ
155
1.7B
1024
4.17
175.1
Discrete diffusion models
VQ-Diffusion 15
518M
100
11.89
Informed corrector 58
230M
17
6.45
185.9
Masked models
MaskGIT 55]
227M
8
6.18
182.1
0.05
MAGE 227
230M
20
6.93
Draft-and-revise
371M
5.45
172.6
StralT 40
863M
12
3.97
214.1
w/ learnable guidance
DPC
26
391M
180
4.45
244.8
MaskGIT Token-Crite
25
422M
36
4.69
174.5
w/ sampling hyperparameter search
MaskGIT +AutoNAT
194M
12
4.45
193.3
0.07
w/ continuous tokens
MAR-B 29
208M
64
4.33
172.4
0.18
2DTV2-XL/2
676M
250
5.06
155.6
MaskGIT + GIVT
304M
16
4.64
Ours
MaskGIT-r
194M
12
5,49
205.6
0.07
16
4.46
196.3
0.10
20
4.18
194.0
0.12
4.09
188.4
0.14
32
3.97
184.9
0.19
MaskGiTTr(cache)RRCCAAP
194M
8+8
5.02
166.6
0.05
12+4
4.50
192.1
0.08
15+5
4.23
193.4
0.09
16+8
4.09
186.5
0.11
22+10
3.98
183.8
0.14
sampling of all masked tokens from the conditional distribution Pr((e) (X, x((t=)), where Ti (t) is the sampling temperature scheduling function.
For each masked position i Mr, the confidence score is computed as:
c = (X x((1)
(1-1))
(4)
The subset S, is then sampled without replacement from M, according to the normalized probabili- ties:
(1)
Softmax
(5)
(t(t)
where r2(-) is the choice temperature seheduling function. practice, this sampling procedure efficiently implemented using the Gumbel- Top-k trick, which provides numerically stable way to sample from categorical distribution while preserving the original ranking based on confidence scores.
15

---

D Continuous-valued MGMs
Apart from discrete-valued tokenizers, some approaches omit the quantization step and directly generate continuous-valus tokens [29], where each X; is continuous embedding. For modeling, continuous-valued MGMs additionally incorporate diffusion process for reconstructing the masked tokens. Specifically, the transformer first produces continuous embeddings 21:N = faam (TM) e RNxd_ At masked positions i, the embedding 2, serves as a noisy latent variable from which the ground-truth token x; reconstructed by modeling the conditional probability p(z; using per-token diffusion loss:
C(2,, Be,la [|le ((x,tta. 2.a))
where ta is the diffusion timestep, (0, I), and denotes a denoising MLP model. The gradients from this loss with respect 2 are backpropagated to update the parameters of Transformer.
E
Additional Experiment Results
E.1
System Comparison Class-conditional Generation w/o CFG
Table 3 compares our method with a wide range of strong generative baselines. MaskGIT-r, obtained by simply increasing the number of decoding steps and adjusting the sampling schedule, already outperforms many approaches with sophisticated designs, such as Token Critic [25] and DPC [26]. Token-Critic
which require additional modules or learnable guidance. Remarkably, at 32 steps. MaskGIT-r matches the performance of StralT 40 a significantly larger model that performs hierarchical modeling. Crucially, our ReCAP-enhanee variant further improves upon MaskGIT-r, improving efficiency for free without retraining or architectural modifications.
E.2 Cost Breakdown of MAR
Table 4: Cost Breakdown of MAR Models. Diff Time denotes the time spent on denoising MLP per image. ReCAP configurations show the (#Full-FE #Local-FFF) steps structure.
Model
#Params
NFE
FID
Time(s)
Diff Time(s)
MAR-I.
479M
256x2
1.76
1.20
0.47
128x2
1.79
0.60
0.25
64x2
1.83
0.31
0.14
20x2
3.12
0.14
0.086
MAR-L+ReCAP
(72+24)x2
1.77
0.37
0.16
(64+20)x2
1.80
0.33
0.14
(20+8)x2
2.41
0.145
0.08
MAR-H
943M
256x2
1.56
2.40
1.03
128x2
1.59
1.20
0.54
48x2
1.69
0.47
0.23
MAR-H+RECAP
(96+32)x2
1.57
1.00
0.46
(36+12)x2
1.69
0.40
0.20
As shown in Table the original MAR architecture uses 100 denoising MLP steps, while our ReCAP implementation reduces this to 50 steps for Local-FE for further acceleration. The remaining computation time primarily comes from attention operations in Transformer blocks, which constitutes the main optimization target of ReCAP. The table demonstrates that ReCAP maintains comparable FID scores while significantly reducing inference time, with the denoising MLP accounting for consistent portion of the total latency across different configurations.
16

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Figure 7: Selected qualitative examples of class conditionll image generation on ImageNet256 using our MAR-L+ReCAP model with (64+20) NFE configuration (FID 1.80, IS 293.9).
E.3 Detailed Results of MAGE and MAGE+ReCAP
As presented in Table 5 evaluate the unconditional generation performance of both MAGE and our proposed MAGE+ReCAR on ImageNet 256x256. The table demonstrates that MAGE+ReCAP achieves comparable FID and scores to the original MAGE while maintaining faster generation speed. Both MAGE and MAGE+ReCAP employ the confidence-basee decaying choice temperature, where the initial temperature is scaled according the total NFE: Timi [6.0, 6.5, 7.0. 8.0, 8.5, 9.0, 9.5, 12.0. 13.0] for NFE E {20, 30, 40, 50, 60, 70, 80, 100, 128) respectively. This progressive temperature scheduling strategy enhances diversity early generation steps while maintaining sample quality in later stages. The (#Full-FE #Local-FF -ocal-FE) step configuration
token sampler with linearly

---

Table Unconditional generation performance on ImageNet 256x256. Results compare MAGE and MAGE+ReCAP across different number of function evaluations (NFE), showing Fréchet Inception Distance (FID 4); Inception Score (IS 1), and generation time. ReCAP configurations show (#Full-FF
+ #l Loal--FE) steps structure.
Method
#Params
NFE
FID
Time(s)
ADM
554M
26.2
39.70
MaskGIT
203M
20.7
42.08
MAGE
439M
20
9.10
105.1
0.245
30
8.44
116.1
0.366
40
8.04
122.3
0,487
50
7.66
123.9
0.608
60
7.47
125.7
0.729
70
7.42
127.3
0,85
80
7.33
128.3
0.971
100
7.29
124.6
1,215
128
7.12
125.4
1.56
MAGE+RcCAP
20+20
8.26
110.2
0.271
25+25
7.89
117.3
0.335
30+30
7.57
117.4
0.399
35+35
7.46
121.8
0.463
40+40
7.38
124.9
0.527
50+50
7.25
124.3
0.654
80448
7.14
126.2
1.018
ReCAP provides flexible trade-offs between quality and speed, with all variants outperforming previous baselines like ADM and MaskGIT.
E.4 Hyperparameter Ablations
We provide more detailed analysis of the key hyperparameters in ReCAP, namely the number of Full-FE (7) and Local-FE (T") steps, as well as the Local-FE insertion length I. Overall, we observe that ReCAP's performance is robust across wide range of hyperparameter settings. Even under more aggressive caching configurations (e.g.. /= = 3), ReCAP consistently outperforms naive step reduction terms of quality-efficiency trade-offs.
Setup. Let u denote the point where Local-FE begins to be inserted, and control the number of Local-FE steps inserted per grouped decoding step. These implicitly determine T and T' via:
S u S-
T=ut
1+1
where is the total number of sampling steps. In the main paper, we used a simple setting of T+T" and / 1. All inference times reported below are measured on an NVIDIA RTX 4090 - GPU with a batch size of 32.
Baseline.
Table the results of MaskGIT-r without ReCAP.
Table 6: MaskGIT-r (baseline).
#Step (S)
FID 4
Time (s) |
12
5.49
0.042
16
4.46
0.054
20
4.19
0.069
24
4.09
0.083
32
3.97
0.110
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Effect of u and We next vary and I to study their influence on ReCAP's behavior.
Conclusion ReCAP consistently improves the quality-efficienge trade-off across different showing robustness to scheduling. Smaller u favors efficiency but may slightly degrade quality since earlier cached features less stable. As increases, cached representations stabilize and the improvement becomes more pronounced.
Table 7: Results for lwa+TT'
#Step (S)
FID
Time (s) 4
16
0
5.02
0.032
2
5.01
0.035
4
4.78
0.038
4.52
0.042
8
4.50
0,044
20
0
4.61
0.040
2
4.54
0,043
4.50
0.046
6
4.39
0.049
8
4.33
0.051
10
4.23
0.054
24
0
4.57
0.047
4
4.42
0.054
8
4.23
0.058
4.14
0.062
12
4.09
0.065
32
0
4.34
0.061
4
4.24
0.068
4.20
0.073
10
4.13
0.077
3.98
0.080
Conclusion 2. Increasing further enhances efficiency while maintaining competitive quality. Even with aggressive caching (1 3), ReCAP still outperforms simple step reduction. However, larger / increases sensitivity to u, requiring careful scheduling to avoid cache degradation.
Table 8: Results for a:a4 2.
#Step (S)
FID
Time (s)
5.82
0.034
7
4.62
0,041
4.50
0.045
20
8
4.51
0,048
11
4.36
0.054
14
4.27
0.058
24
9
4.42
0.055
12
4.18
0.061
15
4.15
0.065
32
14
4.07
0.075
17
3.97
0.081
Reducing Full-FE Steps. While our default configuration in the main paper employs more Full-FE than Local-FE steps, this already yields substantial improvements, underscoring ReCAP's practicality even under balanced schedule. We further explore dynamic caching strategies that reduce the number of Full-FE steps less than half of the total while maintaining or even improving generation quality.
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Table 9: Results for
a:04 T
#Step (S)
u
FID
Time (8) +
16
4
6.33
0.035
4.55
0.043
20
4
5.65
0.039
4.63
0.048
12
4.24
0.055
5.06
0.053
12
4.24
0.061
4.08
0.068
32
12
4.42
0,069
4.10
0.076
20
3.99
0.084
Motivated by our previous findings that later-stage features are more stable and cacheable, we adopt hybrid cache schedule in MAGE+ReCAP for unconditional generation. Specifically, and adaptively increase from to when the number remaining masked tokens falls below 128 (half of the 256-token sequence). Table 10 compares the baseline, static ReCAP (1 1), and hybrid ReCAP (1 E (1,2]).
0
Table 10: Hybrid scheduling enables fewer Full-FE steps while preserving quality.
Steps (base)
FID
Time
ReCAP 1) Steps
FID
Time
ReCAP (1 E (1,2)) Steps
FID
Time
20
9.10
0.15
8.44
0.23
40
8.04
0.31
20+20
8.26
0.17
18+22
8.24
0.16
50
7.66
0.38
25+25
7.89
0.21
23+27
8.01
0.20
60
7.47
0.46
30+30
7.57
0.26
27433
7.59
0.24
70
7.42
0.54
35435
7.46
0.30
32438
7.47
0.27
7.33
0.60
40440
7.38
0.34
36444
100
7.29
0.75
50+50
7.25
0.42
45+55
7.26
0.39
Summary.
The results demonstrate that:
ReCAP with hybrid scheduling achieves comparable or better FID with lower latency.
At 80 total steps, hybrid ReCAP attains 7.38 FID in 0.31s, outperforming the baseline (8.04 FID, 0.31s) and static ReCAP (7.38 FID, 0.34s).
As the total step count increases, the efficiency benefit scales further, indicating potential for even larger gains on longer sequences.
These findings confirm that ReCAP's efficiency gain is not inherently bounded by the number of Full-FE steps. Instead, adaptive scheduling of Local-FE FE insertions enables significant acceleration with minimal quality loss, highlighting ReCAP's flexibility and extensibility for future generative models.
E.5 Extension to Text-to-Image Models
We further evaluate the generality ReCAP by applying it to the Harmon-0.5R text-to-image (T2I) model 53 at higher 512x512 resolution on the MJHQ-30k benchmark.
Baseline. We reproduce the baseline results using the official implementation, measuring inference time on an NVIDIA A800 GPU with a batch size of 50. To ensure a fair comparison, the latency of the denoising MI. module excluded from all measurements, Table [ reports the baseline performance of Harmon-0.5B without ReCAP.
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Table 11: Baseline performance of Harmon-0.5B T2I model at 512x512 resolution.
#Step
FID +
Time (s) 4
32
6.519
0.325
48
6.481
0.490
64
6.461
0.657
Applying ReCAP. We apply ReCAP with varying (u, configurations, where T and T denote the number of Full-FE and Local-FE steps, respectively. Results are summarized in Table 12
Table 12: Harmon-0.5B with ReCAP 512x512 resolution. T #Full-FE, T" #Local-FE.
#Step
(u,1)
FID 4
Time (s) 4
48
(24, 2)
32
12
6.489
0.299
48
(16, 1)
32
6,483
0.318
64
(16, 2)
32
32
6,467
0.347
64
(32, 1)
48
6,463
0.453
Conclusions. model:
ReCAP consistently enhances the quality-efficiency trade-off of the base Harmon
Compared to the 64-step baseline (FID 6.461, 0.657s), ReCAP (32 + 32) achieves compara- ble quality (FID 6.467) with nearly 2x faster inference (0.347s).
With the same number of Full-FE steps (T 48), ReCAP (48 + 16) attains slightly better FID (6.463 vs. 6.481) and is faster (0.453s vs. 0.490s) than the 48-step baseline. The improvement likely arises because the Harmon model incorporates causal LLM component-altien although remains fixed, the input token lengths to the LLM differ, reducing computation under ReCAP.
In the first three configurations, larger T under the same 7 consistently yields better FII) with only marginal latency increases, demonstrating the effectiveness of more aggressive caching (larger l).
Summary. These results further confirm that ReCAP generalizes well to high-resolution and multi- modal text-to-image models, maintaining strong generation quality while substantially improving inference efficiency.
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Answer: [Yes]
The abstract and introduction clearly articulate the core contributions of the paper: introducing ReCAP, plug-and-play module that accelerates masked generative model (MGM) inference by interleaving standard full evaluations with lightweight partial evaluations that reuse cached attention features. These claims are substantiated in Sections 5, and supported by extensive empirical results across three diverse MGM architectures (MaskGIT, MAGE, MAR) in Section 6. The reported improvements in inference cost and efficiency-fidelity trade-off directly reflect the paper's scope and contributions.
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The limitations of ReCAP are explicitly discussed in section 7 (Limitations
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28


Reviews:
[{"review_id": "TZaDBBZGkg", "reviewer": "Reviewer_Rsgx", "summary": "This submission proposes an optimization for inference with Masked Generative Modelling inspired by KV-caching techniques used in Autoregressive models.\n\nThe key observation is that, in MGMs, the attention keys and values (KVs) of previously decoded (context) tokens evolve slowly across decoding steps, and thus could be approximated via caching.\nThe authors thus present ReCAP, an inference algorithm for MGM that combines standard Full-Function Evaluation (Full-FE) steps and Local-Function Evaluation (Local-FE) steps.\n\nIn a Full-FE step, the model selects a group of target tokens to unmask and compute fresh KV for all tokens, but caching KV only for the non-selected tokens (context or masked but not flagged for unmasking).\nA Full-FE step is followed by a succession of Local-FE steps that progressively unmask the target tokens, recomputing KV only for the target tokens and using the cache for the rest.\n\nThe authors show that, as an inference-only strategy, ReCAP can be applied without retraining to various MGM models, both discrete or continuous.", "questions": "My main question relates to the stability and sensitivity of ReCAP's hyperparameters (T, T′) allocation:\n- How does the efficiency-quality trade-off evolve under different allocations of Full-FE (T) and Local-FE (T′) steps?\nIn particular, what happens when ReCAP operates under more aggressive schedules, with a much higher ratio of Local-FEs to Full-FEs? Is there a clear point where failure modes emerge?\n- While I understand that the authors chose simple, generalizable settings across models, is there substantial headroom left if hyperparameters are finely tuned for each architecture? Also, is the behavior of performance vs. hyperparameter choice consistent across different models, or does it vary widely?\n\nSome more systematic analysis of ReCAP’s hyperparameter sensitivity would both help practitioners estimate deployment effort and offer additional insight into the inner workings of MGMs.\n\nAlso, it's mostly a detail but I'd be curious to know if there a reason why, between two not Local-FE steps, the previously unmasked tokens KV are not added to the cache but appears to be recomputed in Fig3.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The paper addresses inference cost, a well-identified bottleneck in generative models.\n- The proposed solution is derived from a common and effective practice in autoregressive models (KV caching), which has been lacking in MGMs. It is simple, does not require retraining, and is easy to integrate.\n- The core hypothesis --stability of KV features across decoding steps-- is well supported by empirical evidence given in the paper. This provides both motivation for the method and interesting insight into MGM behavior.\n- Experiments performed on different MGMs show the general applicability of the approach. The inclusion of continuous-valued models is a welcome addition.\n\nWeaknesses:\n- From a user perspective, deciding how many Full-FE steps (T), how many Local-FE steps (T′), and when to start interleaving them (u) will be critical for balancing speed and quality. The paper provides somewhat limited guidance on how to choose these parameters in practice. Table 1 offers some insight, but only for MaskGIT-r, and the settings there appear somewhat ad hoc (e.g., not always following $u= \\frac{T+T'}{2}$ that seems to be the default for the other experiments). Furthermore, there is no discussion on whether these hyperparameters generalize well across models.\n- The results focus on regimes where ReCAP causes minimal quality degradation. However, it would be interesting to know how the performance evolves in a wider range of values for the repartition between T and T', and when the generation starts to collapse. Currently, the paper leaves these questions open.\n- While a 2–2.5× speedup is certainly valuable, it feels somewhat modest for inference optimization methods.", "was_revised": true, "final_justification": "Technically strong submission, well executed, and that tackles an very relevant problem \nMy concerns has been adressed, so I maintain my recommendation for acceptance.", "rebuttal": "We thank the reviewer for the thoughtful and constructive feedback, and for recognizing the motivation, design, and empirical validation of ReCAP to be sound and broadly applicable. To further address the reviewer’s concerns regarding the sensitivity and generality of ReCAP’s hyperparameters, we have conducted additional ablation studies and implemented a more dynamic and efficient caching schedule, as well as a new evaluation on a text-to-image (T2I) generation task. These results offer deeper insight into the behavior of ReCAP and demonstrate its robustness across a range of settings.\n\n> How does the efficiency-quality trade-off evolve under different allocations of Full-FE (T) and Local-FE (T′) steps? In particular, what happens when ReCAP operates under more aggressive schedules?\n\nWe thank the reviewer for their valuable feedback. While the choice of Full-FE ($T$) and Local-FE ($T'$) steps is central to our method, we find that **ReCAP’s performance is robust across a wide range of these hyperparameters. Even with more aggressive schedules (e.g., $l=3$), ReCAP outperforms naive step reduction.** Below, we conduct a comprehensive evaluation across different allocations of $T$ and $T'$, examining their impact on quality-efficiency trade-offs.\n\nSpecifically, $u$ denotes the point when Local-FE begins to be inserted, and $l$ controls the number of Local-FE steps inserted per grouped decoding step. These implicitly determine $T$ and $T'$, with $T = u + \\frac{S - u}{l+1}$ and $T' = S - T$ ($S$ is the total sampling step). The following tables report the performance with varying $u$ and $l$ (All reported inference time is measured using a single NVIDIA RTX 4090 GPU with a batch size of 32):\n\n\n**MaskGIT-r (Baseline):**\n| #Step(S) | FID  | Time  |\n|-------|------|-------|\n| 12    | 5.49 | 0.042 |\n| 16    | 4.46 | 0.054 |\n| 20    | 4.19 | 0.069 |\n| 24    | 4.09 | 0.083 |\n| 32    | 3.97 | 0.110 |\n\n**MaskGIT-r+ReCAP:**\n\n**Conclusion1:** ReCAP consistently improves quality-efficiency trade-offs over the baseline across different $u$, demonstrating robustness. Smaller $u$ enables better efficiency but may lead to greater quality degradation, as early context features are relatively unstable. As $u$ increases, cached features become more stable and improvements become more significant.\n\n1. $l_{u:u+T'}=1$\n\n| #Step(S) | u | FID | Time |\n|-------|---|-----|------|\n| 16 | 0 | **5.02** | **0.032** |\n| | 2 | 5.01 | 0.035 |\n| | 4 | 4.78 | 0.038 |\n| | 6 | **4.52** | **0.042** |\n| | 8 | 4.50 | 0.044 |\n| 20 | 0 | 4.61 | 0.040 |\n| | 2 | 4.54 | 0.043 |\n| | 4 | 4.50 | 0.046 |\n| | 6 | 4.39 | 0.049 |\n| | 8 | 4.33 | 0.051 |\n| | 10 | **4.23** | **0.054** |\n| 24 | 0 | 4.57 | 0.047 |\n| | 4 | 4.42 | 0.054 |\n| | 8 | 4.23 | 0.058 |\n| | 10 | 4.14 | 0.062 |\n| | 12 | **4.09** | **0.065** |\n| 32 | 0 | 4.34  | 0.061 |\n| | 4 | 4.24 | 0.068 |\n|| 8 | 4.20 | 0.073 |\n| | 10 | 4.13 | 0.077 |\n| | 12 | **3.98** | **0.080** |\n\n**Conclusion2:** Increasing $l$ further enhances efficiency while maintaining quality. Even with aggressive caching (e.g., $l=3$), ReCAP outperforms naive step reduction. However, larger $l$ increases sensitivity to $u$, requiring careful scheduling to avoid excessive cache degradation.\n\n2. $l_{u:u+\\frac{T'}{2}}=2$ \n\n| #Step(S) | u | FID | Time |\n|-------|---|-----|------|\n| 16 | 4 | 5.82 | 0.034 |\n| | 7 | 4.62 | 0.041 |\n| | 10 | **4.50** | **0.045** |\n| 20 | 8 | 4.51 | 0.048 |\n| | 11 | 4.36 | 0.054 |\n| | 14 | 4.27 | 0.058 |\n| 24 | 9 | 4.42 | 0.055 |\n| | 12 | **4.18** | **0.061** |\n| | 15 | 4.15 | 0.065 |\n| 32 | 14 | **4.07** | **0.075** |\n| | 17 | **3.97** | **0.081** |\n\n3. $l_{u:u+\\frac{T'}{3}}=3$ \n\n| #Step(S) | u | FID | Time |\n|-------|---|-----|------|\n| 16 | 4 | 6.33 | 0.035 |\n| | 8 | **4.55** | **0.043** |\n| 20 | 4 | 5.65 | 0.039 |\n| | 8 | 4.63 | 0.048 |\n| | 12 | **4.24** | **0.055** |\n| 24 | 8 | 5.06 | 0.053 |\n| | 12 | 4.24 | 0.061 |\n| | 16 | **4.08** | **0.068** |\n| 32 | 12 | 4.42 | 0.069 |\n| | 16 | 4.10 | 0.076 |\n| | 20 | **3.99** | **0.084** |\n\n\n> While I understand that the authors chose simple, generalizable settings across models, is there substantial headroom left if hyperparameters are finely tuned for each architecture? Also, is the behavior of performance vs. hyperparameter choice consistent across different models?\n\nWe appreciate the reviewer’s insightful question. The overall behavior of performance vs. hyperparameter choice is **largely consistent across models**. For instance, a larger $u$—which delays the use of Local-FE—typically leads to more stable caching and better quality. Based on this observation, we adopt simple and generalizable hyperparameter settings (e.g., $u = \\frac{T + T'}{2}$, $l = 1$) in our main results and demonstrate the broad applicability and robustness of ReCAP across different MGMs.\n\nNevertheless, **the sensitivity to these hyperparameters does vary by model**. Some models, such as MAGE, are more robust to early caching with smaller $u$ without significant quality degradation. Others, such as MaskGIT and MAR, benefit more from conservative schedules with larger $u$. Therefore, **further performance gains can be achieved by fine-tuning these hyperparameters for each architecture**. For instance, in the main paper, we adopt a more aggressive configuration ($u = 0$, $l = 1$) for MAGE to maximize efficiency.\n\nTo explore this further, we include two additional experiments:\n\n**(i) Dynamic caching strategy for MAGE+ReCAP:**\nWe implement a **adaptive cache schedule** with $u = 0$, and adaptively increase $l$ from 1 to 2 when the number of remaining masked tokens falls below 128 (i.e., half of the 256-token sequence). This is motivated by the observation that features in later decoding stages are more stable and thus more cacheable.\n\n| Steps (base) | FID  | Time | ReCAP ($l=1$) Steps | FID  | Time | ReCAP ($l∈{1,2}$) Steps | FID  | Time     |\n| ------------ | ---- | ---- | --------------------- | ---- | ---- | -------------------------- | ---- | -------- |\n| 20           | 9.10 | 0.15 | —                     | —    | —    | —                          | —    | —        |\n| 30           | 8.44 | 0.23 | —                     | —    | —    | —                          | —    | —        |\n| 40           | 8.04 | 0.31 | 20+20                 | 8.26 | 0.17 | 18+22                      | 8.24 | 0.16 |\n| 50           | 7.66 | 0.38 | 25+25                 | 7.89 | 0.21 | 23+27                      | 8.01 | 0.20 |\n| 60           | 7.47 | 0.46 | 30+30                 | 7.57 | 0.26 | 27+33                      | 7.59 | 0.24 |\n| 70           | 7.42 | 0.54 | 35+35                 | 7.46 | 0.30 | 32+38                      | 7.47 | 0.27 |\n| 80           | 7.33 | 0.60 | 40+40                 | 7.38 | 0.34 | 36+44                      | 7.38 | 0.31 |\n| 100          | 7.29 | 0.75 | 50+50                 | 7.25 | 0.42 | 45+55                      | 7.26 | 0.39 |\n\nThese results show that ReCAP with adaptive scheduling achieves comparable or better FID than fixed schedules with better efficiency. For example, at 80 steps total, hybrid ReCAP reaches 7.38 FID in 0.31s, outperforming the base model (8.04 FID,0.31s) and the static ReCAP (7.38 FID, 0.34s), highlighting ReCAP’s future potential with more dynamic and principled schedules.\n\n**(ii) ReCAP for a T2I model (Harmon-0.5B [1]) under different hyperparameters:**\nWe evaluate ReCAP in a 512×512 text-to-image setting using Harmon-0.5B on MJHQ30K. \n\n**Baseline (Harmon-0.5B T2I, 512×512):**\n\n| #Step | FID   | Time  |\n| ----- | ----- | ----- |\n| 32    | 6.519 | 0.325 |\n| 48    | 6.481 | 0.490 |\n| 64    | 6.461 | 0.657 |\n\n**Harmon-0.5B+ReCAP (T = #Full FE, T' = #Local FE):**\n\n| #Step | (u, l)  | T  | T' | FID       | Time      |\n| ----- | ------- | -- | -- | --------- | --------- |\n| 48    | (24, 2) | 32 | 12 | 6.489     | 0.299 |\n|       | (16, 1) | 32 | 16 | 6.483     | 0.318     |\n| 64    | (16, 2) | 32 | 32 | 6.467     | 0.347 |\n|       | (32, 1) | 48 | 16 | 6.463 | 0.453 |\n\nThese results indicate that a larger $l$ (e.g., $l = 2$) is particularly beneficial in this T2I setting, offering stronger acceleration without degrading FID. For example, ReCAP at (16, 2) achieves FID 6.467 in 0.347s, matching the 64-step baseline with \\~2× lower latency.\n\n\n**Together, these results demonstrate that while a general hyperparameter configuration works well across models, ReCAP allows for further tuning and adaptation, and holds great promise when combined with more dynamic caching strategies tailored to specific architectures or tasks.**\n\n[1] Wu, S., Zhang, W., Xu, L., Jin, S., Wu, Z., Tao, Q., ... & Loy, C. C. (2025). Harmonizing visual representations for unified multimodal understanding and generation. arXiv preprint arXiv:2503.21979.\n\n> If there is a reason why, between two not Local-FE steps, the previously unmasked tokens KV are not added to the cache but appear to be recomputed in Figure 3.\n\nIn our design, each Full-FE aims to recompute precise and up-to-date features for all tokens. Because MGMs rely on bidirectional attention, the features of previously unmasked tokens can change across steps as more context is revealed."}, {"review_id": "vj67WMCWXY", "reviewer": "Reviewer_xUBN", "summary": "ReCAP is a method designed to accelerate masked generative models by reusing context features, achieving up to 2.4× faster inference with minimal quality degradation across different architectures and tasks. The authors present experimental results to support their claims, demonstrating improved speed with comparable generation quality. However, I have several concerns about the paper. First, the reported 2.4× speedup is achieved under optimal conditions, while the generation quality drops significantly in certain cases, which is unacceptable for practical use. Second, the effectiveness of ReCAP on masked text-to-image (T2I) models remains unexplored, leaving a critical gap in the evaluation. Overall, I find the contribution of the paper to be limited. The current method does not offer a substantial improvement in speed or quality, and additional revisions are needed. Enhancing the approach to deliver more consistent and competitive acceleration would be necessary for this work to be considered a significant contribution.", "questions": "See weaknesses.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n1. The paper presents strong empirical evidence, such as feature similarity analysis, to support its key assumption regarding the stability of context features during decoding.\n\n2. The writing is clear and well-organized, complemented by intuitive figures and detailed implementation descriptions that enhance both understanding and reproducibility.\n\n3. Additional experiments provided in the appendix add depth and comprehensiveness to the paper.\n\n**Weaknesses:**\n\n1. The core idea bears similarities to TeaCache, but the paper neither cites nor discusses this related work, which could be seen as a significant oversight.\n\n2. Although ReCAP achieves up to a 2.4× speedup, this is not very competitive compared to one-step generators like Flux, which can generate images in a single step without the need for classifier-free guidance (CFG). Furthermore, masked generation models currently lag behind diffusion models in terms of generation quality, which limits the practical applicability of this approach.\n\n3. The paper lacks text-to-image (T2I) experiments. While I understand the method is designed to be lightweight and plug-and-play, it would significantly strengthen the work to demonstrate its effectiveness in the T2I setting. For example, applying it to a model like Meissonic.\n\n4. The results in Table 1 show a notable performance drop for MaskGIT when reducing the number of steps to 16, with the FID score worsening from 4.46 to 5.02. Additionally, the acceleration on MAR-H appears insufficient and may not justify the trade-off in performance.", "was_revised": true, "final_justification": "I will keep my ratings (4).", "rebuttal": "We sincerely appreciate the reviewer’s insightful and constructive feedback. Thank you for recognizing the strengths of our work. We hope the following responses will fully address your concerns and further clarify our contributions. Specifically, we have conducted comprehensive ablations under various settings, expanded our evaluation to include a text-to-image (T2I) generation task, and discussed related work such as TeaCache and Flux. These additions further justify the robustness, practical applicability, and future potential of ReCAP.\n\n> The results in Table 1 show a notable performance drop for MaskGIT when reducing the number of steps to 16, with the FID score worsening from 4.46 to 5.02. \n\nWe thank the reviewer for the careful observation. We would like to clarify that the result reported in Table 1 of the main paper with (FID 5.02, 0.032s) at 16 steps corresponds to ReCAP with $u = 0$ and **still largely outperforms the base model** (FID 5.49 with 0.042s with 12 steps) in quality-speed trade-offs. Specifically, the parameter $u$ denotes the point at which Local-FE begins to be inserted, offering a flexible trade-off between quality and efficiency. In the following, we provide ReCAP performance under varying $u$ and demonstrate that **the effectiveness of ReCAP is robust** to various configurations. \n\n**MaskGIT-r (Baseline):**\n| #Step(S) | FID  | Time  |\n|-------|------|-------|\n| 12    | 5.49 | 0.042 |\n| 16    | 4.46 | 0.054 |\n| 20    | 4.19 | 0.069 |\n| 24    | 4.09 | 0.083 |\n| 32    | 3.97 | 0.110 |\n\n**MaskGIT-r+ReCAP:**\n\n| #Step(S) | u | FID | Time |\n|-------|---|-----|------|\n| 16 | 0 | **5.02** | **0.032** |\n| | 2 | 5.01 | 0.035 |\n| | 4 | 4.78 | 0.038 |\n| | 6 | **4.52** | **0.042** |\n| | 8 | 4.50 | 0.044 |\n| 20 | 0 | 4.61 | 0.040 |\n| | 2 | 4.54 | 0.043 |\n| | 4 | 4.50 | 0.046 |\n| | 6 | 4.39 | 0.049 |\n| | 8 | 4.33 | 0.051 |\n| | 10 | **4.23** | **0.054** |\n| 24 | 0 | 4.57 | 0.047 |\n| | 4 | 4.42 | 0.054 |\n| | 8 | 4.23 | 0.058 |\n| | 10 | 4.14 | 0.062 |\n| | 12 | **4.09** | **0.065** |\n| 32 | 0 | 4.34  | 0.061 |\n| | 4 | 4.24 | 0.068 |\n|| 8 | 4.20 | 0.073 |\n| | 10 | 4.13 | 0.077 |\n| | 12 | **3.98** | **0.080** |\n\nAs shown in the table, ReCAP consistently improves quality-efficiency trade-offs over the baseline across different $u$, demonstrating robustness. Smaller $u$ enables better efficiency but may lead to greater quality degradation, as early context features are relatively unstable. As $u$ increases, cached features become more stable and improvements become more significant.\n\n> It would significantly strengthen the work to demonstrate its effectiveness in the T2I setting.\n\nWe thank the reviewer for the constructive suggestion. We have incorporated additional experiments by applying ReCAP to the **Harmon-0.5B T2I model from [1]** at **512×512 resolution** on the **MJHQ-30k** benchmark.\n\nWe first reproduce the baseline results using the official implementation (inference time measured on NVIDIA A800 with batch size = 50). To ensure fair comparison, we exclude the latency of the denoising MLP module:\n\n**Baseline (Harmon-0.5B T2I, 512×512):**\n\n| #Step | FID   | Time  |\n| ----- | ----- | ----- |\n| 32    | 6.519 | 0.325 |\n| 48    | 6.481 | 0.490 |\n| 64    | 6.461 | 0.657 |\n\n**Harmon-0.5B+ReCAP (T = #Full FE, T' = #Local FE):**\n\n| #Step | (u, l)  | T  | T' | FID       | Time      |\n| ----- | ------- | -- | -- | --------- | --------- |\n| 48    | (24, 2) | 32 | 12 | 6.489     | 0.299 |\n|       | (16, 1) | 32 | 16 | 6.483     | 0.318     |\n| 64    | (16, 2) | 32 | 32 | 6.467     | 0.347 |\n|       | (32, 1) | 48 | 16 | 6.463 | 0.453 |\n\nReCAP consistently improves the quality-efficiency trade-off of the base T2I model:\n\n* Compared to the 64-step baseline (FID 6.461, 0.657s), **ReCAP (32 + 32)** achieves similar FID (6.467) with nearly **2× faster inference** (0.347s).\n* With the same number of Full-FE steps (T = 48), **ReCAP (48 + 16)** yields **better FID (6.463 vs. 6.481)** and is **faster (0.453s vs. 0.490s)** than the 48-step baseline.\n  This efficiency gain is likely due to the Harmon model incorporating a causal LLM: although the number of Full-FE steps is the same, the **input token lengths to the LLM differ**, leading to reduced computation in ReCAP.\n* Moreover, in the first three rows, **larger T' under the same T consistently leads to better FID with only marginal latency increase**, showcasing the strength of aggressive caching (larger $l$).\n\n**These results further validate the generality of ReCAP on high-resolution and multimodal T2I models.**\n\n[1] Wu, S., Zhang, W., Xu, L., Jin, S., Wu, Z., Tao, Q., ... & Loy, C. C. (2025). Harmonizing visual representations for unified multimodal understanding and generation. arXiv preprint arXiv:2503.21979.\n\n> The core idea bears similarities to TeaCache\n\nWe thank the reviewer for pointing out this connection. TeaCache is a representative work on caching for continuous diffusion models, which also leverages temporal similarities between high-level features for acceleration. Our work targets token-based masked generative models (MGMs) / masked diffusion models (MDMs), where the decoding dynamics and caching strategies differ. We will include a discussion of TeaCache in the next version of the paper.\n\n> ReCAP is not very competitive compared to one-step generators like Flux. Furthermore, MGMs currently lag behind diffusion models, which limits the practical applicability of this approach.\n\nWe appreciate the reviewer bringing up Flux as example of one-step generators that offer fast image generation. However, Flux is fundamentally based on rectified-flow transformer architectures with distillation-based guidance, which represents a **distinct paradigm** from token-based masked generative models (MGMs). Flux requires specialized architecture design and supervised distillation training. In contrast, **ReCAP is a training-free, plug-and-play module** specifically designed for token-based MGMs, where generation naturally involves multiple iterative decoding steps.\n\nMoreover, we respectfully note that the **generation quality of MGMs has been rapidly improving**. Recent models such as MAR have significantly closed the gap with state-of-the-art continuous diffusion models employing advanced sampling strategies. In addition, MGMs enjoy efficiency advantages over traditional diffusion models. More importantly, MGMs, as sequence modeling frameworks, have the potential to **unify language and image generation** under a single architecture. This makes them particularly well-suited for **multimodal large language models (MLLMs)**. Notably, more recent work such as **Mmada** [2] demonstrates that MGMs can serve as a strong backbone for large-scale multimodal generative models, further highlighting the practical applicability and future potential of ReCAP.\n\n[2] Yang, L., Tian, Y., Li, B., Zhang, X., Shen, K., Tong, Y., & Wang, M. (2025). Mmada: Multimodal large diffusion language models. *arXiv preprint* arXiv:2505.15809.\n\n> Additionally, the acceleration on MAR-H appears insufficient and may not justify the trade-off in performance.\n\nWe thank the reviewer for the comment. We would like to highlight that ReCAP achieves a 2.4× speedup over the state-of-the-art FID of MAR-H baseline, with minimal performance drop. Moreover, as shown in Figure 5 of the main paper, MAR-H + ReCAP matches the performance of REPA [3], even though REPA is further equipped with advanced guidance interval sampling strategies. Notably, REPA is a state-of-the-art flow-matching and represents a very strong baseline, as it incorporates self-supervised features from powerful pretrained vision foundation models, resulting in rich semantic priors and high-quality generation. In contrast, ReCAP is entirely training-free and does not rely on any external guidance—yet still achieves comparable performance. This highlights the effectiveness and applicability of ReCAP.\n\n[3] Yu S, Kwak S, Jang H, et al. Representation alignment for generation: Training diffusion transformers is easier than you think[J]. arXiv preprint arXiv:2410.06940, 2024."}, {"review_id": "mF8GxJlBZW", "reviewer": "Reviewer_76YC", "summary": "This paper introduces Reused Context-Aware Prediction (ReCAP), a plug-and-play module designed to accelerate inference in masked generative modeling (MGM). By reusing cached key-value (KV) pairs, ReCAP replaces costly full evaluation steps with computationally efficient ones. Experiments demonstrate its effectiveness across multiple representative MGM works.", "questions": "none.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper is well written and easy to follow.\n2. The idea of re-using KV cache is simple and effective.\n3. Comprehensive experiments validate ReCAP’s improvements across three MGM methods.\n\nWeaknesses:\nI do not see much apparent weakness in this paper. However, the current design depends on manual heuristics to determine where ReCAP steps replace full FE steps. While effective, this limits generality.", "was_revised": true, "final_justification": "I have no major concerns about this paper and uphold my decision to accept it.", "rebuttal": "Thank you for your positive feedback. We appreciate your recognition of the paper’s clarity, the effectiveness of the proposed approach, and the breadth of experimental validation across multiple models."}, {"review_id": "vgQWex6T7s", "reviewer": "Reviewer_TV6d", "summary": "Masked Generative Models (MGMs) have shown impressive performance for image synthesis. This paper tackles a redundant operation in decoding steps and proposes a ReCAP, similar to the KV cache in LLMs. Grouped decoding pipeline offers a flexible trade-off between efficiency and quality. The method is compared on the ImageNet benchmark on common metrics across various MGMs.", "questions": "The parameters $u$, $T$, and $T'$ play a crucial role in managing the trade-off between quality and efficiency. However, in all experiments, these values are selected empirically without adequate justification. How are these parameters chosen, or is there a principled approach for selecting them? Reporting performance variations based on different hyperparameter settings would enhance the paper and provide a more comprehensive understanding of their impact.", "limitations": "- While Full and Local FE provide flexible trade-offs, the Local-FE steps ($l_{u+1:u+T'}$) are constrained to 1, in addition to the parameter $u$, which further limits the upper bound of efficiency improvements. Thus, the efficiency gain could be marginal, as it requires Full-FE for more than half of the total steps.\n- All experiments are conducted on 256 resolutions with class conditional generation. Although ImageNet 256 is a comprehensive dataset, conducting experiments on a larger resolution (512) or Text-to-Image (T2I) model, such as [2] will make the paper more complete.\n\n[2] Wu, S., Zhang, W., Xu, L., Jin, S., Wu, Z., Tao, Q., ... & Loy, C. C. (2025). Harmonizing visual representations for unified multimodal understanding and generation. arXiv preprint arXiv:2503.21979.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strenghts\n- The motivation and its analysis are clear.\n- The proposed method that extends KV cache to MGMs with a grouped decoding framework is reasonable.\n- The paper is well-written, and the visualization of motivation and method is well-made.\n\nWeaknesses\n- **Comparison with Recent Work.** Although the authors compare MaskGIT-r+ReCAP with various MaskGIT variants, recent work that utilizes learnable guidance [1] demonstrates superior FID-efficiency trade-offs, achieving an FID of 3.35 with 24 NFEs using MaskGIT, while MaskGIT-r+ReCAP achieves an FID of 3.98 with 22+10 NFEs. I wonder if ReCAP can be integrated with the approach in [1], or if not, the authors should provide a comparison between these methods.\n\n- **Ablation on $l_{u+1:u+T'}$**. While Fig.3 illustrates that $l_{u+1:u+T'}$ could be set to 2 or higher, the actual implementation uses a value of 1, in all experiments, including confidence-based sampling. \nGiven this, it would be valuable to analyze whether reducing the total sampling timestep is more beneficial than setting $l_{u+1:u+T'}$ to a value greater than 1. The paper lacks analysis on this aspect. For instance, which config shows better quality-efficiency using $(u,T,T')=(8,12,4)$ or $(0,8,16)$?\n\n[1] Hur, J., Lee, D., Han, G., Choi, J., Jeon, Y., & Kim, J. (2024). Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance. Advances in Neural Information Processing Systems, 37, 130977-130999.", "was_revised": true, "final_justification": "Most of my concerns are resolved, including compatibility with variant models or sample strategies and robustness on hyperparameter settings. In this regime, I decided to adjust my rating accordingly.", "rebuttal": "We thank the reviewer for the thoughtful and constructive feedback. We appreciate your recognition of the reasonableness of our method and the quality of our presentations. To address your concerns, we have conducted additional experiments and analyses. These include comprehensive ablations over key hyperparameters, an evaluation on a 512×512 text-to-image (T2I) task, and a comparison and discussion of learnable guidance methods such as [1]. These additions further justify the robustness and general applicability of ReCAP across various generation settings.\n\n> Reporting performance variations based on different hyperparameter settings would enhance the paper.\n\n> It would be valuable to analyze whether reducing the total sampling timestep is more beneficial than setting l to a value greater than 1.\n\nWe thank the reviewer for their valuable feedback. While the choice of Full-FE ($T$) and Local-FE ($T'$) steps is central to our method, we find that **ReCAP’s performance is robust across a wide range of these hyperparameters. Even with more efficient caching (e.g., $l=3$), ReCAP outperforms naive step reduction.**\n\nSpecifically, $u$ denotes the point when Local-FE begins to be inserted, and $l$ controls the number of Local-FE steps inserted per grouped decoding step. These implicitly determine $T$ and $T'$, with $T = u + \\frac{S - u}{l+1}$ and $T' = S - T$ ($S$ is the total sampling step). In the main paper, we set $u = \\frac{T+T'}{2}$ and $l = 1$ for simplicity. Below, **we expand our analysis by varying both $u$ and $l$** （Inference time is measured using an NVIDIA RTX 4090 GPU with a batch size of 32）:\n\n**MaskGIT-r (Baseline):**\n| #Step(S) | FID  | Time  |\n|-------|------|-------|\n| 12    | 5.49 | 0.042 |\n| 16    | 4.46 | 0.054 |\n| 20    | 4.19 | 0.069 |\n| 24    | 4.09 | 0.083 |\n| 32    | 3.97 | 0.110 |\n\n**MaskGIT-r+ReCAP:**\n\n**Conclusion1:** ReCAP consistently improves quality-efficiency trade-offs over the baseline across different $u$, demonstrating robustness. Smaller $u$ enables better efficiency but may lead to greater quality degradation, as early context features are relatively unstable. As $u$ increases, cached features become more stable and improvements become more significant.\n\n1. $l_{u:u+T'}=1$\n\n| #Step(S) | u | FID | Time |\n|-------|---|-----|------|\n| 16 | 0 | **5.02** | **0.032** |\n| | 2 | 5.01 | 0.035 |\n| | 4 | 4.78 | 0.038 |\n| | 6 | **4.52** | **0.042** |\n| | 8 | 4.50 | 0.044 |\n| 20 | 0 | 4.61 | 0.040 |\n| | 2 | 4.54 | 0.043 |\n| | 4 | 4.50 | 0.046 |\n| | 6 | 4.39 | 0.049 |\n| | 8 | 4.33 | 0.051 |\n| | 10 | **4.23** | **0.054** |\n| 24 | 0 | 4.57 | 0.047 |\n| | 4 | 4.42 | 0.054 |\n| | 8 | 4.23 | 0.058 |\n| | 10 | 4.14 | 0.062 |\n| | 12 | **4.09** | **0.065** |\n| 32 | 0 | 4.34  | 0.061 |\n| | 4 | 4.24 | 0.068 |\n|| 8 | 4.20 | 0.073 |\n| | 10 | 4.13 | 0.077 |\n| | 12 | **3.98** | **0.080** |\n\n**Conclusion2:** Increasing $l$ further enhances efficiency while maintaining quality. Even with aggressive caching (e.g., $l=3$), ReCAP outperforms naive step reduction. However, larger $l$ increases sensitivity to $u$, requiring careful scheduling to avoid excessive cache degradation.\n\n2. $l_{u:u+\\frac{T'}{2}}=2$\n\n| #Step(S) | u | FID | Time |\n|-------|---|-----|------|\n| 16 | 4 | 5.82 | 0.034 |\n| | 7 | 4.62 | 0.041 |\n| | 10 | **4.50** | **0.045** |\n| 20 | 8 | 4.51 | 0.048 |\n| | 11 | 4.36 | 0.054 |\n| | 14 | 4.27 | 0.058 |\n| 24 | 9 | 4.42 | 0.055 |\n| | 12 | **4.18** | **0.061** |\n| | 15 | 4.15 | 0.065 |\n| 32 | 14 | **4.07** | **0.075** |\n| | 17 | **3.97** | **0.081** |\n\n3. $l_{u:u+\\frac{T'}{3}}=3$\n\n| #Step(S) | u | FID | Time |\n|-------|---|-----|------|\n| 16 | 4 | 6.33 | 0.035 |\n| | 8 | **4.55** | **0.043** |\n| 20 | 4 | 5.65 | 0.039 |\n| | 8 | 4.63 | 0.048 |\n| | 12 | **4.24** | **0.055** |\n| 24 | 8 | 5.06 | 0.053 |\n| | 12 | 4.24 | 0.061 |\n| | 16 | **4.08** | **0.068** |\n| 32 | 12 | 4.42 | 0.069 |\n| | 16 | 4.10 | 0.076 |\n| | 20 | **3.99** | **0.084** |\n\n> ReCAP requires Full-FE for more than half of the total steps.\n\nWe thank the reviewer for the insightful observation. While our default setting in the main paper uses more Full-FE than Local-FE steps, this design already leads to noticeable improvements, demonstrating the practicality of ReCAP even under a balanced schedule. In the following, we further explore **more adaptive and efficient** caching strategies and **demonstrate that the number of Full-FE steps can be reduced to less than half of the total steps**, while still achieving strong generation quality and even **better quality-efficiency trade-offs**. \n\nSpecifically, motivated by our earlier ablations that features in later decoding stages are more stable and cacheable, we implement a dynamic cache schedule in MAGE+ReCAP for unconditional generation. We set $u = 0$ and **adaptively increase $l$ from 1 to 2 when the number of remaining masked tokens falls below 128** (i.e., half of the 256-token sequence). Below we compare baseline, static ReCAP ($l=1$), and hybrid ReCAP ($l\\in\\{1,2\\}$):\n\n| Steps (base) | FID  | Time | ReCAP ($l=1$) Steps | FID  | Time | ReCAP ($l\\in\\{1,2\\}$) Steps | FID  | Time     |\n| ----| ---- | ---- | --------- | ---- | ---- | ------ | ---- | -------- |\n| 20 | 9.10 | 0.15 | —        | —    | —    | —         | —    | —      |\n| 30      | 8.44 | 0.23 | —         | —    | —    | —      | —    | —        |\n| 40    | 8.04 | 0.31 | 20+20        | 8.26 | 0.17 | 18+22    | 8.24 | 0.16 |\n| 50   | 7.66 | 0.38 | 25+25        | 7.89 | 0.21 | 23+27    | 8.01 | 0.20 |\n| 60     | 7.47 | 0.46 | 30+30   | 7.57 | 0.26 | 27+33          | 7.59 | 0.24 |\n| 70  | 7.42 | 0.54 | 35+35      | 7.46 | 0.30 | 32+38      | 7.47 | 0.27 |\n| 80   | 7.33 | 0.60 | 40+40   | 7.38 | 0.34 | 36+44  | 7.38 | 0.31 |\n| 100  | 7.29 | 0.75 | 50+50     | 7.25 | 0.42 | 45+55          | 7.26 | 0.39 |\n\nThese results confirm that:\n\n* ReCAP with adaptive scheduling achieves **comparable or better FID with lower latency**.\n* For example, at 80 steps total, hybrid ReCAP reaches 7.38 FID in 0.31s, outperforming the base model (8.04 FID,0.31s) and the static ReCAP (7.38 FID, 0.34s).\n* As steps increase, the efficiency benefit scales, showing potential for even larger gains with longer sequences.\n\nThis demonstrates that ReCAP’s efficiency gain is not inherently bounded by needing many Full-FE steps — instead, careful scheduling (e.g., hybrid $l$) enables fewer Full-FE steps with strong performance, highlighting ReCAP’s flexibility and future potential.\n\n> A larger resolution (512) or Text-to-Image (T2I) model (e.g., [2]) will make the paper more complete.\n\nWe thank the reviewer for the constructive suggestion. We have incorporated additional experiments by applying ReCAP to the **Harmon-0.5B T2I model from [2] at 512×512 resolution** on the MJHQ-30k benchmark.\n\nWe first reproduce the baseline results using the official implementation (inference time measured on NVIDIA A800 with batch size = 50 and the latency of the denoising MLP module is excluded):\n\n**Baseline (Harmon-0.5B T2I, 512×512):**\n\n| #Step | FID   | Time  |\n| ----- | ----- | ----- |\n| 32    | 6.519 | 0.325 |\n| 48    | 6.481 | 0.490 |\n| 64    | 6.461 | 0.657 |\n\n**Harmon-0.5B+ReCAP:**\n\n| #Step | (u, l)  | T  | T' | FID       | Time      |\n| ----- | ------- | -- | -- | --------- | --------- |\n| 48    | (24, 2) | 32 | 12 | 6.489     | 0.299 |\n|       | (16, 1) | 32 | 16 | 6.483     | 0.318     |\n| 64    | (16, 2) | 32 | 32 | 6.467     | 0.347 |\n|       | (32, 1) | 48 | 16 | 6.463 | 0.453 |\n\nReCAP consistently improves the quality-efficiency trade-off of the base T2I model:\n\n* Compared to the 64-step baseline (FID 6.461, 0.657s), **ReCAP (32 + 32)** achieves similar FID (6.467) with nearly **2× faster inference** (0.347s).\n* With the same number of Full-FE steps (T = 48), **ReCAP (48 + 16)** yields **better FID (6.463 vs. 6.481)** and is **faster (0.453s vs. 0.490s)** than the 48-step baseline. This efficiency gain is likely due to the Harmon model incorporating a causal LLM: although the number of Full-FE steps is the same, the **input token lengths to the LLM differ**, leading to reduced computation in ReCAP.\n* Moreover, in the first three rows of the second table, **larger T' under the same T consistently leads to better FID with only marginal latency increase**, showcasing the strength of aggressive caching (larger $l$).\n\n**These results further validate the generality of ReCAP on high-resolution and multimodal T2I models.**\n\n> I wonder if ReCAP can be integrated with the approach in [1], or if not, the authors should provide a comparison between these methods.\n\nWe thank the reviewer for highlighting recent advances in learnable guidance methods such as [1]. Importantly, the two methods are **orthogonal in both design and objective**: [1] improves quality by enforcing semantic consistency through a learned smoothing encoder. In contrast, ReCAP reuses cached context features to construct low-cost decoding steps, efficiently approximating the effect of multiple full-feature evaluations while maintaining consistency.\n\nMoreover, **ReCAP is fully compatible with the approach in [1]**. Combining the two is straightforward—ReCAP’s caching mechanism can be applied to both the base model and the TOAST module in [1] to further improve the quality-efficiency trade-off.\n\nWe also note a key distinction: [1] requires **additional training** on top of the base model to learn the guidance network, whereas **ReCAP is entirely training-free** and can be directly applied to any pretrained MGM. We will include a discussion of [1] in the next version of the paper.\n\n[1] Hur, J., Lee, D., Han, G., Choi, J., Jeon, Y., & Kim, J. (2024). Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance. Advances in Neural Information Processing Systems, 37, 130977-130999.\n\n[2] Wu, S., Zhang, W., Xu, L., Jin, S., Wu, Z., Tao, Q., ... & Loy, C. C. (2025). Harmonizing visual representations for unified multimodal understanding and generation. arXiv preprint arXiv:2503.21979."}]

Author rebuttal:

Meta-review: This paper introduces a caching mechanism to accelerate masked generative models. The paper is well-written and easy to follow. It addresses a well-identified bottleneck in generative models.The proposed solution extends KV-caching to MGMs and requires no retraining. It is simple and effective, backed by comprehensive experiments. The major concerns raised by the reviewers are related to hyperparameter settings and comparison to recent works. The authors addressed these concerns adequately in the rebuttal and discussions. The final ratings are 2 "accept" and 2 "borderline accept". It is recommended to accept this paper for its solid technical contribution.
Decision: Accept (poster)