PTQ4ARVG: Post-Training Quantization for AutoRegressive Visual Generation Models
Authors: Xuewen Liu
Zhikai Li
Jing Zhang
Mengjuan Chen
Jianquan Li
Qingyi Gu
Venue: ICLR 2026 Poster (2026)
Keywords: Model Quantization
Autoregressive Visual Generation Models
Primary area: optimization
TL;DR: This paper proposes a novel and efficient post-training quantization framework for autoregressive visual generation models.

Abstract: AutoRegressive Visual Generation (ARVG) models retain an architecture compatible with language models, while achieving performance comparable to diffusion-based models. Quantization is commonly employed in neural networks to reduce model size and computational latency. However, applying quantization to ARVG remains largely underexplored, and existing quantization methods fail to generalize effectively to ARVG models. In this paper, we explore this issue and identify three key challenges: (1) severe outliers at channel-wise level, (2) highly dynamic activations at token-wise level, and (3) mismatched distribution information at sample-wise level. To these ends, we propose PTQ4ARVG, a training-free post-training quantization (PTQ) framework consisting of: (1) Gain-Projected Scaling (GPS) mitigates the channel-wise outliers, which expands the quantization loss via a Taylor series to quantify the gain of scaling for activation-weight quantization, and derives the optimal scaling factor through differentiation. (2) Static Token-Wise Quantization (STWQ) leverages the inherent properties of ARVG, fixed token length and position-invariant distribution across samples, to address token-wise variance without incurring dynamic calibration overhead. (3) Distribution-Guided Calibration (DGC) selects samples that contribute most to distributional entropy, eliminating the sample-wise distribution mismatch. Extensive experiments show that PTQ4ARVG can effectively quantize the ARVG family models to 8-bit and 6-bit while maintaining competitive performance.

Published as a conference paper at ICLR 2026
PTQ4ARVG: POST-TRAINING QUANTIZATION FOR AUTOREGRESSIVE VISUAL GENERATION MODELS
Xuewen Liu Zhikai Li";; Jing ? Mengjuan Chen', Jianquan Li", Qingyi Gu
Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences (1 11iuxeewen202,, zhikai.li, 1i, gingyi. gu @ Pia.... ac.en
ABSTRACT
AutoRegressive Visual Generation (ARVG) models retain architecture compati- ble with language models, while achieving performance comparable to diffusion- based models. Quantization is commonly employed in neural networks to reduce model size and computational latency. However, applying quantization to ARVG remains largely underexplored, and existing quantization methods fail to generalize effectively to ARVG models. In this paper, we explore this issue and identify three key challenges: (1) severe outliers at channel-wise level, (2) highly dy- namic activations at token-wise level, and (3) mismatched distribution information at sample-wise level. To these ends, we propose PTQ4ARVG, training- freee post-training quantization (PTQ) framework consisting of: (1) Gron-Proeectedd Scaling (GPS) mitigates the channel-wist outliers, which expands the quantiza- tion loss via Taylor series to quantify the gain of scaling for detivattionweiieee quantization, and derives the optimal scaling factor through differentiation. (2) Static Token-Wise Quantization (STWQ) leverages the inherent properties of ARVG, fixed token length and position-invarian distribution across samples, to address token- wise variance without incurring dynamic calibration overhead. (3) Distribution-Guinn Guided Calibration (DGC) selects samples that contribute most to distributional entropy, eliminating the sample-win distribution mismatch. Exten- sive experiments show that PTQ4ARVG can effectively quantize the ARVG family models to 8-bit and 6-bit while maintaining competitive performance. Code is available at http://gttubb.com/Bieee how../////eee..lly///AAAA
INTRODUCTION
Recently, motivated by the success of autoregressive generation in large language models (LLMs) (Touvron et al., 2023; at., 2024a) and the rising demand from multimodal tasks (Ramesh et al., 2021; Wang et al., 2021), research in visual generation has shifted back toward autoregressive paradigms. A growing number of autoregressive visual generation (ARVG) models (Tian et al., 2024; Wang et al., 2024; Yu et al., 2024; He et al., 2025; Yao et al., 2024; Chen et al., 2025; Liu et al., diffuuson models (Li et al., 2025) in
2024b; Li et al., 2024b) have emerged, surpassing state-of-the image generation. However, the large model sizes and iterative token predictions impose substantial memory and computational overhead, significantly limiting their applicability and generalization. For instance, VAR-d30 (Tian et al., 2024), RAR-XXL (Yu et al., 2024), and MAR- (Li et al., 2024b) contain 2B, 1.5B, and IB parameters, respectively, while the 3B-parameter PAR (Wang al., 2024) model requires more than 3 seconds to generate single image.
Quantization discretizes floating-poin parameters into integers, thereby reducing both model size and computational cost. It is typically categorized into Quantization-Awee Training (QAT) (Li & Gu, 2023; Liu al., 2024c; Esser et al., 2019) and fost-Training Quantization (PTQ) (Li et al., 2022: Liu et al., 2024d; 2025; Li et al., 2023b). While QAT maintains performance through full-model retraining, it demands large amounts of training data and expensive resources. contrast, PTQ requires only a small calibration and does not rely on model training, making it more desirable for compressing and accelerating ARVG models.
"Corresponding author; (zhikai.li, gingyi.gu) @ia.ac.cn.

---

Published as a conference paper at ICLR 2026
Volue
Fink
Choring 939
Somple
(a) input of MHSA module
(b) input of AdaLN module
(c) input of MHSA.proj proj layer
(d) output of block
Figure 1: Challenges of ARVG quantization. (a) Severe outliers at channel-wise level. (b)(c) Highly dynamic activations at token-wise level. (d) Mismatched distribution information at sample-wise level. Data from the RAR-B block "blocks.23".
To explore effective PTQ methods for ARVG models, we first examine the potential constraints and challenges involved: @ Severe outliers at channel-wise level. The activations adjusted by the AdaLN module, i..., the inputs the Multi-Head Self-Attention (MHSA) and Feedforward Network (FFN), suffer from significant outliers at the channel-wiss level. As shown in Fig. I(a), the activations exhibit extremely wide ranges and substantial variation across channels. @ Highly dynamic activations at token-wise level. To preserve the bidirectional token dependencies (Tian al., 2024) of ARVG, the input the AdaLN module contains positional emhedding information, which displays highly dynamic distributions along the token dimension, as shown in Fig. 1(b). Additionally, as ARVG employs conditional information as the initial token, which are sensitive to quantization, it results in the presence of sink tokens in the activations of all linear layers within MHSA and FFN, as shown in Fig. I(c). 3 Mismatched distribution information at sample-wise level. As shown in Fig. I(d), the network activations exhibit high similarity across input samples, particularly for unconditional samples. This sample-wise redundancy leads to mismatched calibration of quantization parameters. Notably, these challenges emerge across different layers of the network, as shown in Fig. 2, necessitating layer-specific quantization strategies.
Despite previous efforts, the above challenges remain inadequately addressed. For challenge ①, some methods (Ashkboos et al., 2024; Li et al., 2024a) alleviates activation outliers using rota- tion transformation low-rank decomposition; however, these approaches introduce additional overhead (e.g., QuaRot (Ashkboos et al., 2024) incurs a 0.3 x speedup loss on LLaMA2-13B -iittt,, SVDQuant (Li al., 2024a) incurs 0.2x speedup loss on FLUX. I-dev-in44) Moreover, they rely on customized CUDA kernels. By contrast, scaling-based methods (Li et al., 2024c; Shao et al., 2023) address outliers with zero computational overhead. OmniQuant (Shao et al.. 2023) optimizes scaling factors via backpropagation, but suffers from training instability and expensive cost (c.g.. 7.3 hours of training for LLaMA-30B on an A100-80G). On the other hand, existing training-free scaling methods (Xiao et al., 2023; Wei et al., 2023; Li et al., 2023b) are empirically designed. For example, SmoothQuant (Xiao et al., 2023) aligns the range of activations and weights via per-channel averages. RepQ- ViT (Li et al., 2023b) enforces identical activation ranges across channels. Due to lack of theoretical justification, these methods remain suboptimal and offer no guarantee of effectiveness. For challenge ( previous methods (Yao et al., 2022; Dettmers et al., 2022) for LLMs address highly dynamic activations by employing dynamic token- wise quantization. Unfortumately, this introduces additional calibration overhead during inference (e.g.. i.LM.int8 (Dettmers et al., 2022) incurs 0.5x speedup loss on GPT-3-13B), and the min-max calibration results in accuracy degradation (e.g.. 15.3 FID drop on dynamic token-wise quantization for VAR). ViDiT-Q (Zhao et al., 2024) identifies token-wise variance in Diffusion Transformer (Peebles & Xie, 2023) models, but it adopts the same dynamic strategy as used in LLMs. For challenge 3,, current calibration strategies (Shang et al., 2023; Li et al., 2023a; Liu et al., 2024d) focus on temporal- -hipe mismatch in diffusion models EDA EDA-DM (Liu et al., 2024d) extracts samples from various timesteps), yet they fail address sasple-wise mismatch in ARVG models.
To this end, we propose PTQ4ARVG, training-free PTQ framework tailored for ARVG models. (1) We conduct an in-depth analysis of scaling effects on quantization and propose Gain-Projected Scaling (GPS), which mitigates channel-wise outliers and represents the first quantization scaling strategy based on mathematical optimization. GPS accurately quantifies the gain of scaling for quantization and derives the optimal scaling factor through mathematical differentiation. Specifically, we perform Taylor expansion on the quantization loss of activations and weights separately. The gain of scaling is defined as the reduction in activation quantization loss minus the increase in weight quantization loss. By differentiating with respect to the scaling factor, we maximize this gain obtain the optimal
2

---

Published as a conference paper at ICLR 2026
factor that minimizes overall quantization loss. (2) We introduce Static Token-Wise Quantization (STWQ) that assigns fine-grained static quantization parameters to handle highly dynamic activations. Since ARVG generates fixed number of tokens, STWQ allows quantization parameters to be set offtine, introducing no online calibration overhead. Moreover, we reveal the position-invariant distributions of token activations across samples, enabling STWQ with percentile calibration to ensure high accuracy. We also deploy the quantized model to demonstrate the compatibility of STWQ with standard CUDA kernels. (3) We design Eetribution-Guidee Calibration (DGC), which selects samples by evaluating their contribution to the overall distribution entropy. By eliminating redundant samples, DGC enables accurate calibration with a coeriiuttio-matcchen samples. Overall, our contributions are as follows:
We identify three key challenges in quantizing ARVG models: (I) severe outliers at channel- wise level, (2) highly dynamic activations at leknnwwiee level, and (3) mismatched distribu- tion information at sample-wise wise level.
We propose PTQ4ARVG, which includes: (1) GPS leverages mathematical theory derive optimal scaling factors for outlier suppression, (2) STWQ addresses token-wise variance without incurring additional calibration overhead, and (3) DGC selects samples that match the real distribution to ensure accurate calibration.
To the best of our knowledge, PTQ4ARVG is the first comprehensive PTQ framework for ARVG family models. We conduct extensive experiments on VAR, RAR, PAR, and MAR, demonstrating that PTQ4ARVG outperforms existing methods and effectively quantizes models to 6-bit while preserving competitive accuracy.
2 RELATED WORK
AutoRegressive Visual Generation models (Tian et al., 2024; Wang et al., 2024; Yu et al., 2024; Li et al., 2024b) have recently surpassed diffusion models in image generation. More compellingly, their architectural compatibility with LLMs offers great potential for future multimodal integration. As shown in Fig. 2, similar to LLMs, ARVG models rely on an autoregressive transformer architecture to predict the next tokens. However, unlike LLMs with non-fixed token sequence lengths, ARVG predicts fixed number of tokens. In addition, ARVG enforces bidirectional token dependencies by embedding conditioning into the network, which includes positional and class information. Existing ARVG models typically use conditional information as initial token, but differ slightly in token prediction granularity. For example, VAR (Tian et al., 2024) predicts scale tokens at once, RAR (Yu et al., 2024) generates one token at a time, PAR (Wang et al., 2024) first predicts one token sequentially-followe 2024b) predicts multiple random tokens at once. While current models incorporate KV Cache techniques to accelerate inference, the latency is still unsatisfactory. For instance, PAR-3B takes more than seconds to generate one image. Moreover, the challenge of large model size remains unaddressed. These limitations significantly hinder the deployment and scalability of ARVG models on resource-constrainee devices.
by parallel prediction of multiple non-local tokens, and MAR (Li et al.,
Post-Trainins Training Quantization (Wu et al., 2024; Liu et al., 2024d: et 2024a; 2023b; Xiao et al., 2023; Shao ct al., 2023; Ashkboos et al., 2024; Zhang et al., 2025) reduces model size and accelerates inference. For vision transformer models, RiT (Li et al., 2023b) employs reparameterization to address outlier activations. For diffusion models, EDA-DM (Liu et al., 2024d) optimizes the quantization reconstruction loss and introduces temporally aligned calibration. PTQ4DiT (Wu et al., 2024) adjusts activation and weight distributions based on their correlation in temporal networks. SVDQuant (Li et al., 2024a) isolates outliers via low-rank decomposition and designs customized CUDA kernels to fuse related operations. For LLMs, SmoothQuant (Xiao et al., 2023) performs per-channel equivalent scaling to balance the ranges of activations and weights. OS+ (Wei et al., 2023) further aligns all activation channels to a common center. OmniQuant (Shao et al., 2023) optimizes scaling factors via training, while QuaRot (Ashkboos et al., 2024) alleviates outliers based on rotation transformation. Although these methods perform effectively for previous models, they do not work well with ARVG models. SVDQuant relies on customized CUDA kernels and QuaRot introduces extra inference overhead. EDA-DM and PTQ4DiT are tailored to the temporal nature of diffusion models, making them incompatible with ARVG. In addition, other approaches are inadequate for ARVG. LiReVAR (Xie et al., 2024) pioneers the quantization of VAR models, but it only assigns higher precision to coantization-sensitiii layers. Notably, to the best of our knowledge, our method is the first comprehensive PTQ framework specifically designed for ARVG models.
3

---

Published as a conference paper at ICLR 2026
Block X
Scale
Linear
VARI next scale tokens prediction
GELU
FFN
Linear fel
D2
VAE Decoding
Scale. Shift
LayenNorm
target sequence
RAR: next one token prediction
Scale
T1
TI
Linear
Autoregressive Transformer
MatMul
Saftmax
MHSA
PAR: one token prediction multi-tokeni prediction
MatMul
Ti-1
Linear 4kv
c0
c1
c4
cn-1
1
Scale, Shift
input sequence
AdoLN
LoyerNorm
2
TC class taken
image token
Sample Tokens
Conditioning
conditioning position+class
MAR: next multi-random- prediction
Figure 2: Overview of ARVG models. (Left) The autoregressive architecture, mechanism, and challenges of ARVG. (Right) Existing ARVG models with different token prediction granularity.
PRELIMINARY
ARVG Architecture consists of L blocks, each containing a Multi- Self-Attention (MHSA), Feedforward Network (FFN), and an Adaptive LayerNorm (AdaLN), shown in Fig. 2. The AdaLN transforms the conditioning into shift and scale parameters to adjust the activation distribution, thereby preserving bidirectional token dependencies and conditional guidance.
Quantization transforms floating-point tensor æ to integer tensor æ using quantization parame- ters: scale factor ó and zero point The uniform quantization can be formulated as:
== clamp + 0, 2 1), 8
R 1 Rx Tup
Edown:
Edown
(I)
where 1-1 denotes the Dounding-to-neane operator, bit-width b determines the range of clamp function, and Re represents the quantization range [Zup. Ldown. Min-max calibration calculates the Rs using the minimum and maximum values of æ. On the other hand, percentile and MSE calibration utilize the percentile values and the minimum quantization error values of æ, respectively. While the min-max calibration is the fastest, offers the lowest accuracy. To reduce inference overhead, dynamic token-wiss quantization in LLMs typically adopts min-max max calibration.
Equivalent Scaling phrnchannel transformation that offline shifts the quantization difficulty from activations to weights. For linear layer with X E R xx and W E Raxm the output Y X XW. Y R xm where the number of tokens, n the input channel, and m is the output channel. The activation X divides per-in-channel scaling factor E IR and weight W scales accordingly in the reverse direction to maintain mathematical equivalence:
Y (X (s W)
Since the s can be fused into the network weights offline, no additional overhead introduced.
25 sy
R///;
RL/s;
PTQ4ARVG
100%
4.1 CAIN-PROJECTED SCALING
96%
Previous methods primarily focused on Inttiibution-based scaling, relying on empirical intuition to address thannel-wist outliers. Due to lack of theoretical justification, these methods remain suboptimal and offer no guarantee of effectiveness.. To address the above limi- tations, propose Cain-Projected Scaling (GPS), which provides
943
92%
RAR-b
VAR-d16
PAR-XL
Figure 3: When R. R the
statistical results of Remark 1.
4

---

Published as a conference paper at ICLR 2026
mathematical interpretation of how scaling affects quantization, and derives the optimal scaling factor through analytical differentiation:
Step 1: Quantization Loss. We begin by amalyzing the weight-activation quantization loss E (x: W). For a linear layer with activation æ E IR 1x, weight W I _R" and output y E R 1xmm = x W. the overall quantization loss E (æ: W) can be formulated as:
E(x, W) - Ei W) - [(x, W + EiC(2, W) - L(x, W)
(3)
The proof is reported in Appendix A.2. Here, and W denote the quantized values. The first and second term in Eq. 3 represent the quantization loss of activation Eg and weight Ew, respectively.
Step Taylor Expansion. Similar to prior works AdaRound (Nagel et al., 2020) and BRECQ (Li et al., 2021), we first approximate the weight quantization loss using a Taylor series expansion:
1 Ew = E [(z, W) f(x, w)] A H W WWWW AW
where ((W) = IE [ [ is the Hessian matrix, A W represents the quantization enrors of weights. Furthermore, we derive the weight quantization loss for the output y/k- = x W., as follows:
Ew
1
W. AA
(W. AWW.
2
(5)
1
2 C. CE AAW 2212 AW2, 2x22 + + AW, n,,k 2x,²
Here, Vyy is the Hessian of the task loss w.r.t. yk- We approximate the Hessian loss using the MSE loss, and safely omit the cross terms (please see Appendix A.3 for detail). In the same way, the activation quantization loss for yv can be formulated as:
1 Ax" T H (x) Ax 2
1 V
Ex
C. [W1, A W2,4 2 AAz² +... W Ar 2]
(6)
2
Step 3: Introducing Scaling. To clearly illustrate the impact of scaling quantization, we simplify the representations of activation æ E RIx2 weight W E 2x3 output E 1x3, and scaling factor E F 1x2 as follows:
W1, W2,1
W1.2
W1.
x
32
W
W2,2
W2.3
y
y1
V2
V/
|
$1
$2
(7)
Based on Eq. 5 and Eq. 6, the quantization losses of weight and activation for y1 are:
1 [[[W
1 2 [WiidAr + W Arg
Ew ~
+ AW2,1
Ex ~
(8)
2
where since V2, C is identical in Ew., and Ex,, we simplify their expressions accordingly. Fur- thermore, we represent the quantization range of activations along the channel dimension as Rg [R] R2). Since our method adopts percentile calibration, the range after scaling becomes Ry= = [__/s1, $1, R2 /2]. The superscript indicates the values after scaling.
Analyzing the impact of scaling on quantization. We quantify the impact of scaling on quantization based on a remark that the activation channel with the largest range before scaling remains the largest range after scaling, formally stated as Remark 1.
Remark 1. When R Re holds that 8, > 8, and R > R2/s5-
The remark is based on statistical observations: when R Rig, over 98% of channels satisfy s; > sj, and more than 99.5% of channels satisfy R s: R2/ss. sj, as shown in Fig, 3.
Without loss of generality, we assume R1 > R2. Inspired by DilateQuant (Liu et al., 2024c), we Rs quantify the quantization error with Ar 8e x c 2 x c, where the c is a integer deviation constant. Based on per-tensor activation quantization and Remark 1, the quantization error before and after scaling can be formulated as:
R1
R1 Arg
Before: Ar
26
c1,
1
C2
(9)
R1 '1
R2/31 1
After: : Art ~
x e₁
RJ
Ar /81,
Ar's ~
x R Arz/si
(10)
2b 1
5

---

Published as a conference paper at ICLR 2026
Substituting Eq. 2 and Eq. 10 into Eq. 8, the activation quantization loss after scaling is written as:
Exe
E2
W 2
W2,1 2 AArb]]
1 E
(Wi1,
(1)i1-s))((A1// (W2,1 ( (Axe/s))
$2
E
W1,1
A
W2,1 2 A02
(11)
812
Similarly, as proven in Appendix A.5, the weight quantization loss after scaling can be expressed as:
E ~ E AW1.1 2 $1 A22,1 2x22
(12)
In general, 8 >> (by R > ) and $1 > $2 (by Remark 1), leading to Eyg < Eg and E Ew.. Therefore, scaling reduces the activation quantization loss, and the gain can be defined as ب W2,1 PAzz². On the other hand, scaling increases the weight quantization loss, and the added loss can defined as gw., = F' Fw..,
Is E E 201
51 -1"-0²" x2 AW2,1 2x2². 282
Step 4: Closed-form solution of scaling factor. We model a scaling gain function to derive the optimal scaling factor:
(33)) = sw.. 2 (W2,1 (W2, + AW2, 2x2²
1
82 81
51 22,1 2Ar2"
AW2,1 2,1 2x22² (13)
82
Here, 81 denotes the scaling factor for the channel with the largest activation range. We calculate it using 81 /RL/ RR./R,,. which ensures that R2 Rty. In conclusion, we reformulate the problem of determining all scaling factors into solving the remaining scaling factors based on $1. Specifically, $2 is optimized with respect to $1 maximize 9s32). Clearly, this is convex optimization problem, which can be solved by taking the derivative:
(x2) = -- 312
$2 W2,1 2 Da2 +
$1 AW
22
2 222
2
82
AA 2.1
(14)
82
Ar2
The above derivation only minimizes the quantization loss of y1. We further extend it to yields:
g(02)=
1
(W2x Arz 2 AAW,, 2
822
m
W2,
Arz² 2
81
W2. 2022 (15)
2
281 2
282
A22,522
i=1
(16)
W2.Arr
Based on Eq. 16, we maximize scaling gain to obtain the optimal scaling factor that minimizes overall quantization loss. The Algorithm of GPS is illustrated in Appendix F. is worth noting that different V C are unobservable and exhibit slight variations. Fortunately, prior optimization studies (Nagel et al., 2020; Li et al., 2021; Wei et al., 2022) assume them to be a common constant, which does not affect the optimization results. We follow the same assumption in our method.
4.2 STATIC TOKEN-WinE QUANTIZATION
class: 0-100
distributtion
We reveal that ARVG models exhibit highly dynamic ac- tivations token-wise level, characterized by: (I) input of the AdaLN module showing variation along the token dimension (Fig. 1(b)). (2) input of linear layers exist- ing sink tokens (Fig. I(c)). This phenomenon is further analyzed in Appendix H. L.LMS also exhibit token- wise activation variation. However, due to the variable token se- quence lengths and position-uncertain token distributions, only dynamic token-wise quantization (Shao et al., 2023; Dettmers et al., 2022) with online min-max calibration can be applied. This method not only introduces additional overhead during inference but also leads to inaccurate calibration.
class: 100-200
distributtion
-100
32
200
250
200
250
takens
Figure 4: Inputs of AdaLN in RAR-B from different sample class. The distri- bution remains invariant across samples.

---

Published as a conference paper at ICLR 2026
In sharp contrast, ARVG models exhibit two distinctive properties: fixed token sequence lengths and vosition-invarian distribution across samples (as shown in Fig. 4). Building on these properties, propose Static Token-Wain Wise Quantization (STWQ), which sets quantization parameters by offline percentile calibration. Specifically, as shown Appendix Fig. 9, (1) STWQ assigns quantization parameters along the token sequence for AdaLN module. (2) STWQ separately assigns quantization parameters for the sink tokens and normal tokens of linear layers. As reported in Table 5, STWQ introduces no additional calibration overhead while preserving accuracy.
4.3
DISTRIBUTION -UUIDED CALIBRATION
Current calibration research focuses on temporal-wise redundancy in diffusion models. DiTFas- tAttn (Yuan al., 2024) and LiteVAR (Xie et al., 2024) are the first to recognize sample-wise redundancy. They leverages this property for model caching. However, we find that the sasple-wise redundancy also results in mismatched calibration, significantly hindering accurate quantization. To address this, we propose Distribution-Gudder Calibration (DGC), which employs Mahalanobis distance to measure the distributional entropy p of sample x with respect sample set:
( V u)=-
(17)
where u and represent the mean and covariance the sample set, respectively. DGC selects the top 50% of samples with the highest distributional entropy as the calibration.
EXPERIMENT
Models and Metrics. We assess the quantization capabilities of our method on four ARVG models: VAR (Tian et al., 2024), RAR (Yu al., 2024), PAR (Wang et al., 2024), and MAR (Li al., 2024b). All models generate 50K images on ImageNet (Deng et al., 2009) and are evaluated using FID (Heusel al., 2017), sFID, IS (Salimans et al., 2016), and Precision. We also deploy the quantized models on an RTX 3090 GPU to assess real-world acceleration and compression performance.
Table Comparative results for VAR and RAR models.
VAR-dI6
VAR-d24
Bit Width
Methods
IS
FID
sFID
Precision
IS
FID
sFID
Precision
283.21
3.60
8.27
0.85
317.16
2.33
8.24
0.82
SuaothQuant
229.87
4.29
13.39
0.79
246.68
4.42
12.66
0.77
RepQ*
211.21
4.36
13.33
0.76
240.18
4.74
14.10
0.76
OS+
230.41
4.11
12.22
0.79
250.61
12.93
0.77
WSA8
OmniQuant
226.92
4.19
12.49
0.79
244.46
14.98
0.76
QuaRot
231.38
3.99
11.38
0.79
257.71
3.40
13.34
0.77
SVDQuant
229.36
4.11
12.72
0.78
253.78
12.38
0.76
Ours
230.04
4,06
12:23
0.79
252.70
13.24
0.77
SmoothQuant
101.55
18.54
17.22
0.57
178.43
7.93
12.80
0.65
RepQ*
109.97
16.30
15.57
0.59
1601.6
8.84
15.45
0.66
OS+
123.38
13.54
12.68
0.64
191.61
6.54
13.40
0.67
W666
OmniQuant
98.27
22.19
19.44
0.57
115.02
18.35
23.40
0.61
QuaRot
155.76
8.96
13.26
0.67
200.58
5.90
13.68
0.70
SVDQuant
130.15
12.53
15.36
0.60
195.83
6.23
13.12
0.70
Ours
162.85
8.34
12.63
0.68
204.02
5.51
12.73
0.71
Bit Width
Methods
RAR-B
RAR-XI
292.80
1.96
6.16
0.82
308.54
1.54
5.31
0.80
SmoothQuant
242.97
2.80
7.76
0.78
229.06
3.35
8.33
0.74
RepQ*
211.64
4.37
9.70
0.73
212.13
4.22
8.89
0.71
OS+
256.01
2.50
7.93
0.78
238.01
8.53
0.73
WSA8
OmniQuant
281.12
2.23
7.21
0.81
283.67
1.68
5.92
0.78
QuaRot
250.54
3.41
13.00
0.77
279.09
2.06
6.47
0.79
SVDQuant
164.69
11.94
22.09
0.69
279.94
1.86
5.85
0.76
Ours
283.47
2.21
7.22
0.82
304.18
1.58
5.57
0.80
Quatthiuant
31.04
63.77
42.08
0,36
30.00
63.70
40.84
0.36
RepQ*
18.96
82.31
49.57
0.43
22.50
77.11
44.70
0.30
OS+
57.92
40.14
24.58
0.45
19.82
84.63
$4.38
0.30
W6A6
OmniQuant
148.89
11.66
12.89
0.67
150.35
13.31
13.23
0.64
QuaRot
18.86
101.60
38.43
0.32
43.32
54.40
40.40
0.42
SVDQuant
8.97
125.51
65.60
0.39
200.80
5.41
7.75
0.68
Ours
206.17
5.13
12.68
0.75
250.70
2.79
6.70
0.76

---

Published as a conference paper at ICLR 2026
Table 2: Comparative results for PAR models.
PAR-XI.4x
PAR-XXI.4x
Bit Width
Methods
IST
F
sFID
Precision
FID
sFID
Precision
FP
259.2
2.61
0.82
263.2
2.35
0.82
SmoothQuant
8.28
132.17
79.50
0.06
4.35
207.84
152.22
0.15
RepQ
138.97
114.35
0.06
5.90
188.37
101.15
0.14
OS+
7.31
128.72
93.63
0.07
4.89
192.82
125.03
0.14
W8A8
OmniQuant
215.71
3.55
0.78
224.87
3.05
0.78
SVDQuant
213.98
3.80
7.79
0.78
222.74
2.91
6.83
0.78
Ours
219.50
3.55
7.71
0.79
232.16
2.95
6.60
0.79
SmoothQuant
8.08
146.52
69.36
0.05
4,90
175.21
131.43
0.17
RepQ"
5.82
157.07
132.66
0.05
4.04
210.32
139.97
0.12
OS+
7.27
138.78
86,01
0.06
4.05
194.58
144.41
0.22
OmniQuant
15.72
107.29
53.01
0.20
15.15
113.72
73.68
0.18
W6A6
SVDQuant
102.03
19.52
18.19
0.60
101.71
18.80
20.56
0.63
SQ+STWQ
107.33
13.26
7.09
0.59
94.27
14.69
7.73
0.59
RepQ*+STWQ
72.96
23.04
9.93
0.50
82.35
20.74
12.96
0.54
Ours
113.33
12.87
6.80
0.62
119.19
11.05
7.17
0.63
Table 3: Comparative results for MAR models.
MAR-B
MAR-L
MAR-H
Bit Width
Methods
FID
Precision
FID
Precision
FID
Precision
FP
281.7
2.31
0.82
296.0
1.78
0.81
303.7
1.55
0.81
OS+
169.43
5.94
0.69
224.64
2.87
0.73
248.27
2.84
0.73
QuaRot
173.55
6.82
0.70
178.99
7.57
0.68
175.23
9.07
0.68
W8AS
SVDQuant
165.47
6.40
190.18
5.19
233.13
Ours
279.97
2.36
0.82
284.02
1.92
0.80
294.66
1.67
0.79
OS+
109.83
13.56
0.61
141.75
11.02
0.62
174.49
8.44
0.65
QuaRot
140.71
11.97
0.66
137.59
13.85
0.63
122.76
17.79
0.62
W6A6
SVDQuant
51.95
36.14
30.19
68.52
0.33
10.47
142.37
0.18
Ours
249.14
2,99
0.78
254.13
3.12
261.35
2.62
0.75
Quantization and Comparison Settings. PTQ4ARVG applies 6-bit (W6A6) or 8-bit (W8A8)
quantization to all linear layers and matrix multiplications in ARVG models. To highlight the effi- ciency, PTQ4ARVG only selects 128 samples for calibration. Additionally, our method introduces no additional overhead during inference and does not rely on customized CUDA kernel designs. Since dedicated quantization framework cxists for ARVG models, we compare it with representative quanti- zation approaches. including SmoothQuant (Xiao al., 2023), OS+ (Wei al., 2023), RepQ (Li et al., 2023b), OmniQuant (Shao et al., 2023), Quarot (Ashkboos et al., 2024), and SVDQuant (Li al., 2024a). Notably, all methods are evaluated under their default settings.
5.1 MAIN RESULTS
VAR and RAR Model. As reported in Table our method significantly outperforms both training- free OS+ and training-based OmniQuant, improving FID by 35.01 and 6.53 on 6-bit RAR-B, respec- tively. Compared to the rotation-based QuaRot, our method demonstrates greater advantages at lower bit-widths. Moreover, due the distinct activation distributions and autoregressive architecture of ARVG, SVDQuant cannot retain the advantages it demonstrates on diffusion models.
PAR and MAR Model. As reported in Table 2 and Table 3, the OmniQuant and SVDQuant fail 6-bit precision, while our method maintains competitive performance. Notably, we do not compare against QuaRot, as PAR does not meet its requirements: "The formula holds when the number of heads and the dimension of each head are both powers of 2 " More results, including the larger models and W4A8 tasks, are provided Appendix J.
5.2 ABLATION STUDY
FID-SX ismall better) better)
random
We conduct ablation studies using SmoothQuant as the baseline. As reported Table 4, our method consistently improves quanti- zation performance, demonstrating the effectiveness of each pro- posed component. the following, we perform a more fine- grained analysis of each method individually.
17
ours
16
compare GPS with distribution- based scaling methods on RAR-B at 6-bit precision. Specifically, using PTQ4ARVG as the baseline, we replace GPS with different scaling methods, reported in Table 6. Here, SQ+RepQ* denotes first applying SmoothQuant, followed by RepQ*. As can be seen, mathematiiallly-deiivee scaling method outperforms previous
Ablation Study on GPS.
14
140
145
352
158
164
(large better)
Figure 5: Visualizing the Advan- tages of GPS on VAR-d16 at 6- bit precision. approaches. Furthermore,
8

---

Published as a conference paper at ICLR 2026
Table 4: Efficacy of different component in this paper.
Method STWQ
RAR-B
VAR-dI6
Bit Width
GPS
DGC
15
FID 1
sFID 4
Precision
1ST
FID.
sFID
Precision
31.04
63.77
72.08
0.36
101.55
18.54
17.22
0.57
62.47
36.51
24.53
0.46
127.13
13.32
14.41
0.64
W6A6
183.21
6.67
12.74
0.71
161.36
8.75
13.53
0.69
206.17
5.13
12.68
0.75
162.85
8.34
12.63
0.69
validate the optimality of our scaling factor SGPS, we introduce random perturbations within the
range [-0.3saes, +0.3sGPs] to @GPS, and conduct 100 times experiments. As shown in Fig. 5, SGPS achieves the best quantization performance.
Table 6: Ablation experiments of GPS.
Table 5: Ablation experiments of STWQ.
Scaling Method
18
FID L
Precision
Method
1st
FIDI
Precision
Time (ms)
Speedup
Smooth Quant
135.40
10.26
0.68
FP
283.21
3.60
0.85
1163.0
1.000x
RepQ"
92.44
33.79
0.59
SQ (w/ TW)
101.55
18.54
0.57
397.1
2.929x
OS+
161.63
7.71
0.69
SQ+DTWQ
73.05
30.14
0.49
473.9
2,457x
SQ+RepQ*
170.07
7.43
0.70
SQ4STWQ
151.60
10.41
0.67
397.9
2.922
GPS (ours)
206.17
5.13
0.75
Ablation Study on STWQ. We compare STWQ with dynamic token- coken-wise quantization (DTWQ) on 6-bit VAR-d16. As reported Table 5, STWQ outperforms DTWQ in accuracy. Additionally, we deploy the quantized network (8-bit). With a batch size of 100 and a sequence length of
256, DTWQ results in a 0.47x reduction in speedup compared to the case without token-wise quantization. In contrast, STWQ maintains quantization efficiency while preserving high accuracy. The slight differences in speedup arise from operator scheduling rather than dynamic calibration.
Ablation Study on DGC. Pre- vious calibration for diffusion models focused on addressing temporal-wise mismatch. In con- trast, DGC, from a novel perspec- tive, eliminates sample-wise mis- match to improve performance. We compare our method with random and uniform sampling methods, as shown in Fig. 6.
Randdom Uniform
Ours
0.76
195
6.0
0.72
186
5.5
0.70
64
125
0.68
32
188
(a) IS score
(b) FID score
(c) Precision score
Figure 6: Ablation experiments of DGC on RAR-B with 6-bit quantization. The x-axis denotes different calibration size. DGC not only achieves high accuracy but also maintains strong robustness, as evidenced by consistent improvements across all metrics with larger calibration sizes.
FP Ours
Latency (ms)
4500
3722
2000
Memory (MB)
4500
4241
4500
4663
3000
1750
2000
1599
3000
2226
2791
2186
3030
2688
3162
380
747
515
199
1000
0B3
167
531
1500
868
1337
1023
0
64
128
258
84
128
256
64
128
256
64
128
258
RAR-L
VAR-d20
RAR-L
VAR-d20
Figure 7: The PyTorch implementation of PTQ4ARVG on RTX 3090 GPU.
Speedup and Memory Saving. In this section, we deploy the 8-bit RAR-L and VAR-d20 to evaluate the real acceleration and compression performance. We use standard CUDA kernel to deploy the decoder network. Inference latency and peak memory usage are evaluated with a batch size of 100 across varying token sequence lengths. As shown in Fig. 7, PTQ4ARVG achieves 3.01 x speedup and 1 1.92x reduction in peak memory on VAR-d20 when the sequence length is 256.
6 CONCLUSION
In this paper, we focus on introducing the quantization techniques into the realm of ARVG models. Our study reveals the quantization challenges of ARVG models across the channel. token, and sample dimensions. Correspondingly, we propose PTQ4ARVG, framework tailored for ARVG models. Specifically, (1) we propose novel theory-based scaling strategy that quantifies scaling gains in quantization and derives the optimal scaling factor via differen- tiation to address channel outliers. (2) Leveraging ARVG properties, we offline-assign finer-grained quantization parameters to handle highly dynamic token activations. (3) We eliminate redundant samples based on distribution entropy to obtain
training-free and hardware- friendd PTQ
ciniibution-matched calibration. Experiments show that PTQ4ARVG advances in accuracy for ARVG family compared to existing methods, making ARVG models more practical for deployment in resource-constraing environments. We hope our work will further advance the research and applicability of ARVG models.

---

Published as a conference paper at ICLR 2026
7 ACKNOWLEDGE
This work supported in part by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant Number XDB1 1000000000; in part by the National Natural Science Foundation of China under Grant Number 62276255; in part by the Postdoctoral Fellowship Program of CPSF under Grant Number G2C20251175.
REFERENCES
Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman. Quarot: Outlier-free 4-bit inference in rotated Ilms. Advances in Neural Information Processing Systems, 37:100213-100240 2024.
Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. Janus-pro: Unified multimodal understanding and generation with data and model scaling, arXiv preprint arXiv:2501.1783 17811, 2025.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pp. 248-255, Icee, 2009.
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. Gpt3. int8 O: 8-bit matrix multiplication for transformers at scale. Advances in neural information processing systems, 35: 40318-30332. 2022.
Steven K Esser, Jeffrey McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmen- dra S Modha. Learned step size quantization. arXiv preprint arXiv:1902.08153. 2019.
Yefei He, Yuanyu He, Shaoxuan He, Feng Chen, Hong Zhou, Kaipeng Zhang, and Bohan Zhuang. Neighboring autoregressive modeling for efficient visual generation. arXiv preprint arXiv:2503.. 10696, 2025.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
Muyang Li, Yujun Lin, Zhekai Zhang, Tianle Cai, Xiuyu Li, Junxian Guo, Enze Xie, Chenlin Meng, Jun- Yan Zhu, and Song Han. Svdqunat: Absorbing outliers by low-rank components for 4-bit diffusion models. arXiv preprint arXiv:24 1.05007, 2024a.
Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. Advances in Neural Information Processing Systems, 37: 56424-56445, 2024b.
Xiuyu Li, Yijiang Liu, Long Lian, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, and Kurt Keutzer. Q-diffusion: Quantizing diffusion models. Proceedings of the IEEE/CVF International Conference Computer Vision, pp. 17535-17545 2023a.
Yuhang Li, Ruihao Gong. Xu Tan, Yang Yang. Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu. Breeq: Pushing the limit of post-training quantization by block reconstruction. arXiv preprint arXiv:2102.05426. 2021.
Zhikai Li and Qingyi Gu. 1-vit: Integer-only quantization for efficient vision transformer inference. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 17065-17075,
2023.
Zhikai Li, Liping Ma, Mengjuan Chen, Jumrui Xiao, and Qingyi Gu. Patch similarity aware data-free quantization for vision transformers. In European conference on computer vision, pp. 154-170. Springer, 2022.
Zhikai Li, Junrui Xiao, Lianwei Yang, and Qingyi Gu. Repq-vit: Scale reparameterization for post-training quantization of vision transformers. In Proceedings of the IEEE/CVF International Conference Computer Vision, pp. 17227-17236, 2023b.
10

---

Published as a conference paper at ICLR 2026
Zhikai Li, Xuewen Liu, Jing Zhang, and Qingyi Gu. Repquant: Towards accurate post-training quanti- zation large transformer models via scale reparameterization. arXiv preprint arXiv:2402.05628, 2024c.
Zhikai Li, Xuewen Liu, Dongrong Joe Fu, Jianquan Li, Qingyi Gu, Kurt Keutzer, and Zhen Dong. K- sort arena: Efficient and reliable benchmarking for generative models via wise human preferences. In Proceedings of the Computer Vision and Puttern Recognition Conference, pp. 9131-9141, 2025.
Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. Deepseek-v3 technical report. arXiv preprint arXiv:2412. 19437, 2024a.
Dongyang Liu, Shitian Zhao, Le Zhuo, Weifeng Lin, Yu Qiao, Hongsheng Li, and Peng Gao. Lumina- mgpt: Illuminate flexible photorealistic text-to-image generation with multimodal generative pretraining, arXiv preprint arXiv:2408.02667, 2024b.
Xuewen Liu, Zhikai Li, and Qingyi Gu. Dilatequant: Accurate and efficient diffusion quantization via weight dilation. arXiv preprint arXiv:2409. 143077, 2024c.
Xuewen Liu, Zhikai Li. Junrui Xiao, and Qingyi Gu. Enhanced distribution alignment for post-training quantization of diffusion models. arXiv e-prints, pp. arXiv-2401, 2024d.
Xuewen Liu, Zhikai Li, and Qingyi Gu. Cachequant: Comprehensively accelerated diffusion models. arXiv preprint arXiv:2503.cmm 00000.013223 323, 2025.
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort. Up or down? adaptive rounding for post-training quantization. In International conference on machine learning, pp. 7197-7206. PMLR, 2020.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4195 4195-4205, 2023.
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International conference on machine learning, pp. 8821-8831. Pmir, 2021.
Tim Salimans, Ian Goodfellow, Wojeiech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in neural information processing systems, 29, 2016.
Yuzhang Shang, Zhihang Yuan, Bin Xie, Bingzhe Wu, and Yan Yan. Post-training quantization on diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1972-1981, 2023.
Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo. Omniquant: Omnidirectionally calibrated quantization for large language models. arXiv preprint urXiv:2308. 13137, 2023.
Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. Advances in neural information processing systems, 37:84839-8843 2024.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv: 2023.
Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang. Difan Zou, Jiashi Feng, and Xihui Liu. Parallelized autoregressive visual generation. arXiv preprint 1 2024.
Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. Simvlm: Simple visual language model pretraining with weak supervision. arXiv preprint arXiv: 108. /0904, 2021.
11

---

Published as a conference paper at ICLR 2026
Xiuying Wei, Ruihao Gong, Yuhang Li, Xianglong Liu, and Fengwei Yu. Qdrop: Randomly dropping quantization for extremely low-bit post- post-training quantization. arXiv preprint arXiv:2203.05740, 2022.
Xiuying Wei, Yunchen Zhang, Yuhang Li, Xiangguo Zhang, Ruihao Gong, Jinyang Guo, and Xianglong Liu. Outlier suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling. arXiv preprint 00X112230.009155, 2023.
Junyi Wu, Haoxuan Wang, Yuzhang Shang, Mubarak Shah, and Yan Yan. Ptq4dit: Post-training quantization for diffusion transformers. arXiv preprint arXiv22405.16005, 2024.
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models. In International Conference Machine Learning, pp. 38087-38099. PMLR, 2023.
Rui Xie, Tianchen Zhao, Zhihang Yuan, Rui Wan, Wenxi Gao, Zhenhua Zhu, Xuefei Ning, and Yu Wang. Litevar: Compressing visual autoregressive modelling with efficient attention and quantization. arXiv preprint (xXiv2241..17172 2024.
Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, and Yuxiong He. Zeroquant: Efficient and affordable post-training quantization for large-scale transformers. Advances in Neural Information Processing Systems, 35:27 (127166--7783. 2022.
Ziyu Yao, Jialin Li, Yifeng Zhou, Yong Liu, Xi Jiang, Chengjie Wang, Feng Zheng, Yuexian Zou, and Lei Li. Car: Controllable autoregressive modeling for visual generation. arXiv preprint arXiv:2410.04671, 2024.
Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, and Liang-Chiel Chen. Randomized autoregressive visual generation. arXiv preprini DXXi::2411.007700 2024.
Zhihang Yuan, Hanling Zhang, Lu Pu, Xuefei Ning, Linfeng Zhang, Tianchen Zhao, Shengen Yan, Guohao Dai, and Yu Wang. Ditfastattn: Attention compression for diffusion transformer models. Advances in Neural Infommaionn Processing Systems, 37:1 1219,, 2024.
Jing Zhang, Zhikai Li, Chengzhi Hu, Xuewen Liu, and Qingyi Gu. Saq-sam: Semantically-aligned quantization for segment anything model. arXiv preprint arXiv:2503.06511: 2025.
Tianchen Zhao, Tongcheng Fang. Haofeng Huang, Enshu Liu, Rui Wan, Widyadewi Soedarmadji, Shiyao Li, Zinan Lin, Guohao Dai, Shengen Yan, et al. Vidit-q: Efficient and accurate quantization of diffusion transformers for image and video generation. arXiv preprint wrxiv:2406.025-2 025400 2024.
12

---

Published as a conference paper at ICLR 2026
PTQ4ARVG: Supplementary Materials
A
PROOF
A.1
QDrop's Proof of Ea,,, W)
The quantization-ddeuantizzioo process of activation be represented as:
Quant: Ø = clamp (1= x + +)),
DeQuant a = ô (æ 2) =
(18)
where æ denotes the integer value. The introduction of quantization error to x can be expressed as c x=1 + u(x)), where can be defined as:
@ 2)
(19)
@ c)
② +
here, denotes the deviation of the integer value, which is affected by bit-width and rounding error, and can thus be treated as a constant.
Furthermore, Consider matrix-vecte multiplication, The quantized output can be expressed as
9 a aW x ( ④ (1 + V (a))) given by
+ ui (ar) (x)
+ ui (x)
1 un (a:) + Un (x)
1 + u1 (z)
aW-
x 3
W
W O
...
(20)
un
+ un (x)
+ uu
As can be seen, by taking V1, (z) - u, (x). quantization error on the activation vector (1 + u(x)) can be transplanted into perturbation on weight + V())). Thus, the error caused by weight- activation quantization can be briefly expressed as:
E(x, W) =E C(t W) - C(x, w)]
(21)
=E L xx(1 + u(x))) w) - L(x, w)]
(22)
=E L x. W 2 (1+ (1++ L(2, W[2
(23)
A.2
Proof of E(x, W)
The E(x, W) can be formulated as:
E (x, W) = (() W) C(x, w)] E [C(x + Aæ, W + AW) - C(x, W)]
(24)
Here, à and W denote the quantized values, A and AW represent the quantization emror of the activations and weights, respectively. As proven in QDrop (Wei al., 2022), E(xx, W) admits the form:
F(x, W) = E T. (z,
n.
W @ (1 V(=)) (1+ - L(x, w)]
(25)
For convenience, we reproduce Qrrop's derivation Appendix A.1. Evidently, the activation quantization error can be accumulated into the weight quantization emror. Therefore, the intermediate state L(x, W) can be inserted into the Eq. 25:
v. o V - _(x, w) + L(x, W) - C(x, w)] Ela, W) = C
< c x. W o (1+
L(x, w)] + E [(x, W) - L(x, [(x < C(ā, W) C(x, w)] + E C(x,, W) - C(x, w))
(26)
At this point, the E(x, W) is decomposed into the activation quantization loss E, (the first term in Eq. 26) and the weight quantization loss Ew (the second term in Eq. 26).
13

---

Published as a conference paper at ICLR 2026
Extensive prior studies (Xiao al., 2023; Liu al., 2024c; et al., 20236) have shown that, compared activation quantization error, the weight quantization error is minimal (W W) under the weight- activation quantization. To simplify the derivation, we approximate the activation quantization loss, F.g [át, W) C(x,, W) using activation error with the full-precision weights, Ex E (c W) L(x, W).. The rationale for this approximation is given in Appendix A.4. Note that in our code implementation we still compute the activation quantization loss via the Ez. Finally, the quantization loss E(x, W) is formulated as follows:
Exx, W S (á, W) - L(x, W)) + E C(x, W) - L(x, w)
(27)
A.3 Proof of E
Inspired by AdaRound (Nagel et al., 2020), we approximate the weight quantization loss using the mean-squared error (MSE) loss, as formulated below:
Ew
AW
HH WWW.W.W... k
(28)
2
E C. AW T x &AW
(29)
2
2 Vy L. AW. E x AW. k
(30)
2 V L C-E(#AW..))
(31)
where V C is the Hessian of the task loss w.r.t yk- We further demonstrate that the cross terms are negligible and can therefore be safely omitted, justified by the following analysis:
Ew,
V Yk L. C-E((AAW..) .
V L. C.E((EAW, A) (#AW.N)
2
(4)
V C. E
(AW x²) (x)
V
C. E
AW. "( AW
2
(32)
V L-E
AW & AWJATIT
2
L. ΣΣ
AAW, may
2 yk
(8) V C. EE [AWL, 201 + AW2 2222222 +... +.... AW 2xxx,
2
where (a) since x W.., E RI, its transpose equals itself; (b) due to the inherent randomness of rounding-to- neareess quantization, each AW, K. AW, j.A E AW. independent and satisfies B|AW,, j.&] E W E ((W,,, 0 when * j. Therefore, for i + j. i.e., in the case of cross terms, these terms can be safely omitted.
A.4 Proof of Ex En
According to Eq. 6, Fn and F.g can be expressed as:
Es E[ W) L(x, W)]
(33)
1 V L E " AAri + W2. 2 +... + W W.. "Ar," 2]
(34)
2
14

---

Published as a conference paper at ICLR 2026
Eg C W) - ((x, w)]
(35)
1 Vy L E W Ari W A23 + W Aay
V/ f. E (Wi,** 2 AW1, Ari
2
+ (W2,²² + 222 + AW2, 2
+ (Wn, n, 2
+ AW,, A
(36)
Since A AWin is small and E [ = =0, we neglect the higher-order terms in Ez. Therefore, Eg can be numerically approximated by Eg.
A.5 Proof of E
Since weights are quantized per output channel, The quantization range of weight W is denoted as Ryy We also observe that: when > sj. the new quantization range Rly is better approximated by RW 8; (as shown in Fig. 8). So the quantization error for weight before and after scaling can be expressed as:
RW X c3.
R 2h
Before
AW1,
AW2,1
X
(37)
26
B 81
R 81
After: AW1,1 ~
x C3 ~ AW $1, AW ~
x x ~ AW2,1
(38)
2
-1
2 -1
Substituting Eq. and Eq. 38 into Eq. 8, the weight quantization loss after scaling can be written as:
E
E
AW 1,1 2/ + AW, 222
2
(AW1,1 31 2 11) + (AW21 (x)
(39)
AW1, 2x1 2 2+ 81
R
AW2,1 2x22
2
$2
B EXPERIMENT DETAILS
Rw z Rw
In this section, present detailed experimental implementa- tions. The pre-training models of VAR, RAR, PAR, and MAR obtained from the official websites. PTQ4ARVG focuses on quantizing the decoder network. We empoly channel-wiss asymmetric quantization for weights and layer-wise wise asymmetric quantization for activations. After scaling, PTQ4ARVG fuses all scaling factors into the network weights, ensuring zero additional overhead during inference. More specifically, we fuse the scaling factors into the AdaLN weights for RAR and VAR models, and into the weights of attention norm and ffn, norm for the PAR and MAR models. For experimental evaluation, we use the ADM's TensorFlow evaluation suite friiedddiffusion to evaluate FID, sFID, IS, and Precision.
100%
96%
94%
92%
RAR-b
VAR-d16
PAR-XL
Figure 8: When > $j, over 99% of output channels satisfy [ow Rw 8: < R/w 5%).
C BASELINE DETAILS
This section provides detailed implementation specifics for all baseline methods to facilitate re- producibility and comparison. The primary configuration settings reported in Table 7. For scaling-bass based baselines, we apply them to all ARVG layers that can absorb scaling factors, specif- ically the glve and fel layers, and absorb the shifting and scaling factors into the network prior
15

---

Published as a conference paper at ICLR 2026
to inference. For rotation- bassdd methods, since ARVG cannot absorb rotation factors offline, the rotations are computed online during inference. All baselines, consistent with our method, quantize all linear layers and matrix multiplications, including the KV cache, and without relying custom CUDA kernels. The baselines employ a randomly sampled calibration set of size 128. The specific implementation details
follows:
SmoothQuant. SmoothQuant is a training-free free method. In our reproduction, we strictly align all settings with the open-source implementation: channel-wise quantization for weights, layer-wise quantization for activations, and the default smoothing factor set to 0.5.
OS+ OS+ is a training-free method. We reproduce the standard OS+, applying channel-wise quantization for weights and layer-wise quantization for activations, without fine-tuning integration. All settings are strictly aligned with the open-source implementation: the shifting factors are computed from the per-channel min and max values; grid search is used to determine the scaling threshold t that minimizes output error, and channels exceeding & are scaled to obtain the final scaling factors.
RepQ. RepQ is a training- free method. We reproduce RepQ based on the open- implementation. The procedure as follows: first, channel-wise activation quantization parameters are calibrated using the calibration set; next, shifting and scaling factors are computed to unify the activation ranges across channels; finally, the channel-wise activation quantization parameters are reparameterized into layer-wise parameters while preserving channel- -wiee weight quantization. For ARVG softmax activations, we use its logv22 quantizer.
Omni Quant a training-basse method. trainable parameters include two weight clipping factors and two equivalent transformation factors. We retain the default parameter initial- ization and training architecture. The learning rates for the weight clipping factors and equivalent transformation factors
OmniQuant.
set to 5 x 10 and 1 x 10 10-2, respectively. Training is conducted with a batch size of 32 over 20 epochs on a calibration set of 128 images. We apply channel-wise quantization for weights and dynamic token-wise quantization for activations.
SVDQuant. SVDQuant is a training-free free method. It first transfers activation outliers to the weights using a smoothing factor, then represents these outliers in a rank-r branch matrix via singular value decomposition. For evaluation convenience, we do not integrate the branch matrix into the Nunchaku engine; instead, it is computed online during inference and added the quantized weights. This simplification does not affect method performance. We set the smoothing factor to 0.5 and the rank to 32, using dynamic token-wiss wise quantization for activations and channel quantization for weights to match the default settings. Notably, while SVDQuant leaves some layers of diffusion models unquantized, in our reproduction, all ARVG layers are quantized to maintain consistency across all baselines and our method.
QuaRot. QuaRot a training-free method. It mitigates activation outliers using rotation factors based on randomized Hadamard transforms. It exploits the rotation invariance of LayerNorm by applying a pair of inverse rotations to the layer's input and output, thereby reducing input outliers without altering the layer output. However, in ARVG, the adjacent layers adaptive LayerNorm, which does not preserve rotation invariance. As a result, the rotation factors cannot be absorbed offline before inference. Therefore, apply the rotation factors online during inference to handle outliers. Activations are quantized using dynamic token-wise quantization and weights using channel- quantization, consistent with the original method.
Table 7: Implementation details of baselines and PTQ4ARVG.
Method
Weight Quant
Activation Quant
Training
Calibration
Online Compute
SmoothQuant
channel-wise
layer-wise
No
128 random
No
OS+
channel-wise
layer-wise
No
128 random
No
RepQ*
channel-wise
layer-wise
No
128 random
No
OmniQuant
channel-wise
dynamic token-wise
Yes
128 random
No
SVDQuant
channel-wise
dynamic token-wise
No
128 random
Yes
QuaRot
channel-wise
dynamic token-wise
No
128 random
Yes
PTQ4ARVG
channel-wise
STWQ
No
128 DGC
No
16

---

Published as a conference paper at ICLR 2026
D OVERVIEW OF PTQ4ARVG
In this section, we provide an intuitive visualization of PTQ4ARVG in Fig. 9. Our method addresses the challenges across different layers. It is worth noting that the quantization parameters in STWQ are statically set rather than calibrated dynamically during inference.
Block X
(6.4)
(6.4)
Challenge Chima ftoree
Chiere Ringe
Scale
Lineer Tc2
GELU
FFN
Lincar fc1
Scale, Shift
STWQ
LayerNorm
Token
PTQ4ARVG
Tokan Challenge ?
Scale
Linear proj
Town Forge
STWQ
MatMul
Sottmax
MatMul
Lineer giv
colibration
Challenge Sample Etrage
SeTTle Birge
Scale: Shift
DGC
AdaLN
LayerNorm
Sample Tokens
Conditioning
Figure Overview of PTQ4ARVG.
E EMPIRICAL ANALYSIS OF APPROXIMATION BIASES
In this section, we conduct empirical analyses on the three approximations involved in GPS to demonstrate their validity and generality. The three approximations we analyze are:
(I) Hessian approximation of wetivation-weight quantization loss, omitting the MSE cross term. A theoretical justification for this approximation provided in Appendix A.3.
(2) Upper-bound approximation of overall quantization error, where the activation quantization loss b. = E Ca W) - L(x, w) is approximated by Ex = EE [C( W) - L(x, W)]. A theoretical justification for this approximation is provided in Appendix A.4.
(3) Scaling quantization error approximation, in which the estimated activation quantization error after scaling is used to approximate the true scaled quantization error. This approximation builds upon the error-est estimation strategy validated in DilateQuant Liu et al. (2024c).
GPS is only applied to the gkv and fcl layers, where the scaling factors can be absorbed. We evaluate the approximation biases of these two layers across various models and input distributions. Specifically, we meansure this bias RAR, VAR, PAR, and MAR using 64 samples. We compute the error (Approximations 2 and 3) using the L1 norm, while the loss (Approximation 1) is computed using the MSE account for cross-term effects. As a result, the numerical values of the loss and the еrror approximations reported in the table differ. The results are summed over the sample and token dimensions and averaged over the channel dimension.
As shown in Table 8, all three approximations exhibit small biases across different models. layers, and input distributions, empirically supporting the validity of the assumptions underlying GPS.
F
ALGORITHM OF GPS
The GPS can be implemented simply. Taking a linear layer as an example, GPS illustrated in Algorithm More importantly, since GPS inherently reduces quantization loss through stable scaling,
17

---

Published as a conference paper at ICLR 2026
Table Empirical analysis of approximation biases with 64 samples INT6 setting. (-%) denotes the ratio of the approximation bias to its ground-truth value. Here, the superscripts "real" and "appro"
denote the measured true values and the approximations used the paper, respectively, Elies and Elins represent the cross terms of weight-quant loss and activation -quatt loss. "Bias" indicates the bias of the quantization error upper bound, and Aæ' bias denotes the difference between the true
GPS-scaled activation quantization error and the estimated activation error according to our Eq. 10.
RAR-B
VAR-d16
PAR-XI
MAR-B
Approximation
Object
qkv
qkv
Tel
gkv
gkv
Icl
8.6902
2.6107
10.3244
3.0049
63.1687
3.9422
8.6859
2.4609
10.2159
0.8183
6.4346
2,9558
59.9057
0.0043
0.1085
0,0143
3,2630
Hessian loss Approximation
Ew
(0.09%)
(0.05%)
(3.74%)
(1.05%)
(1.71%)
(3.55%)
(1.63%)
(5.17%)
Eg
6.8648
17,4328
6.8574
13,3940
1,5024
12.2295
7.3307
196.6933
Eusspr
6.8587
17,4188
6,6576
13.2114
1,8820
12.2041
7.3214
182,0671
Eliis
0.0061
0.0140
0.1998
0.1826
0,0204
0.0254
0.0093
14.6262
(0.09%)
(0.08%)
(2.91%)
(1.36%%
(1.36%)
(0.21%)
(0.13%)
(7.44%)
Ex
364.62
523,71
526.63
758.
227.50
639.
401.05
2077.32
Upper-bound error Approximation
Ex
364,70
524.10
527.67
758.72
227.54
639.53
401.18
2077.61
Bias
(0.02%)
0,0815
(0.08%)
0,3931
(0,20%)
1.0416
(0.04%)
0.2679
(0.02%)
0.0392
(0.03%)
0.1740
(0.03%)
0.1362
(0.01%)
0.2905
Aw Fent
92.23
108.84
158.57
137.33
183.05
245.78
145.28
440.34
Scaling quantization error Approximation
xf opyrro
88.42
107.53
182.54
145.08
165.47
279.56
144.97
472.78
lile
3,8021
1,2688
23.9728
7.7560
17.5718
33.7800
0.3125
32.4476
(4.12%)
(1.17%)
(15.12%)
(5.65%)
(9.60%)
(13.74%)
(0.22%)
(7.37%)
Algorithm Overall workflow of GPS
Input:
activation X E R1x= and W E R
Output:
optimal scaling factor 8 E IR"
1. Preparing data
X, (X)), Wg Q(W)
obtain quantized values calculate quantization errors
AX X X, AW = W W.,
2. Searching activation channel with the largest range
Rg max [X, dim = 0) min(X, dim 0)[0] R k max(R.)
> calculate activation range find ky channel with the largest activation range
3. Calculating 8k
8k R
calculate 8y that ensures R R
4. Calculating remaining scaling factors based on 8k
for i 1 to do if k:
8 8k
else:
8: = 8k / W Az,
solve the scaling factor 8; based on Eq. 16
end for return
can theoretically serve as plug-and-play tool to enhance quantization performance across diverse frameworks. In future work, we will explore its generalization and applicability to other models.
G VISUALIZATION OF GPS QUANTIZATION ERROR
Prior methods relied on empirically designed scaling factors to mitigate quantization error. In contrast, GPS derives the scaling factor from theoretical principles, explicitly modeling the quantization-error reduction brought by scaling and obtaining a closed-form optimal solution via differentiation. To visualize the advantages of GPS, we evaluate the quantization errors in the first block's s qkv and fcl layers across different models (RAR-B, VAR-d16, PAR-XL, MAR-B), comparing the errors of no scaling, after applying SmoothQuant Xiao et al. (2023), after applying GPS, and after applying GPS with cross terms. As shown in Fig 10, GPS achieves further reduction in quantization error compared with empirically designed scaling methods SmoothQuant. And retaining cross terms has a negligible impact original GPS.
18

---

Published as a conference paper at ICLR 2026
RAR-Qkv
RAR-fc1
VAR-qkv
VAR-fc1
210
194.02
100
350.59
83.59
Quant Error
95
180
85
305
28.74
80
150
230
65.05
64.35
148.58
65
120
95.75
94.85
55
46
155
100.8
96.8
50
90
40
8D
PAR-qkv
PAR-fc1
MAR-qkv
MAR-fc1
44
41.96
170
82
790
7 95
150.85
Quant Error
.0
145
36
120
66
62.44 62.44
570
542.31
32
29.75
95
75.45
76.28
58
54.54
54.62
460
28:25
05
354.88
No-scale
Smooth
GPS
GPS-full
Figure 10: The quantization error of layer output under the INT6 setting using 64 samples. The error is measured with the L1 norm and averaged over tokens.
H ANALYSIS OF CHALLENGE 2
We analyze the underlying causes of highly dynamic activations at the token-wise level. Firstly, to ensure bidirectional dependencies among predicted image tokens, ARVG embeds conditioning information into the network via the AdaLN module. The conditioning includes not only the image conditional information but also the positional information of tokens. We observe that positional information varies across different position in the token sequence, resulting in input of the AdaLN module showing variation along the token dimension. Secondly, previous KV Cache studies on LLMs have identified that the initial token in Attention is highly sensitive to model performance, referring these critical tokens sink tokens. Unlike LLMs, we observe that in ARVG models, sink tokens are present in all linear layers of MHSA and FFN. We attribute this property of ARVG three key factors: (1) ARVG inherently uses class conditions initial tokens, which encapsulate critical class information and play a pivotal role in conditional generation. (2) The initial tokens are visible to all subsequent tokens, making them readily trained serve highly sensitive tokens. (3) The distribution of the initial tokens are significantly different from that of all other tokens.
I VISUALIZATION OF POSITION-INVARIANT DISTRIBUTIONS IN TOKEN DIMENSION
To validate the position-invarians distributions of ARVG activations, we visualize token-wise ac- tivations for VAR-d16 and RAR-B across different layers, classes, and conditioning. Note that these models are trained on ImageNet and cannot generate images from other data distributions. Therefore, we do not verify this property on datasets beyond ImageNet. Since unconditional samples inherently correspond to class label 1000, we can merge the class and conditioning dimensions. For visualization, we select samples with labels 0, 1, 2, 999, and 1000 (unconditional), and visualize the distributions across different layers whenever possible. As shown in Fig. 11 and Fig. 12, the activations of VAR-d16 and RAR-B B exhibit position invariinn distributions, remaining consistent across different classes and conditions. This confirms the motivation and implementation of STWQ.
J ADDITIONAL RESULTS
Table 9 shows the performance of PTQ4ARVG on RAR-L, RAR-XXL, VAR-d20, and VAR-d30. Similar to the PAR models, RAR-XXL does not satisfy the requirements of QuaRot, and thus no conducted this model. For VAR models, while SVDQuant performs well on the FID metric, exhibits poor performance on the IS metric, likely because low-rank decomposition of the weights undermines the model's ability to generate diverse samples.
QuaRot experiments
19

---

Published as a conference paper at ICLR 2026
var.block.0.pet
var.block....gg 1.qkvv
var. blrckbock.7444 7.fc2
var.block.8.fe
dass coodd
-0.0
-1.6%
3.0
0.5
-0.5
1.5
355
3.5
class cond
0.8
2.5
2.5
0.0
1.5
3.3-
-0.8-
150
430
600
300
4200
600
1550
300
450
600
130
430
500
cond
-1.0
600
150
1.6
3.0
999 cond
0.8
1.5
2.5
2.5-
0.0
1.5
3.5-
-0.8
0.5
0.5
-1.0
3.0
0.5
150
300
430
600
300
4000
120
45/0
6.10
1550
45.0
650
uncond
-1.6
300
450
600
150
450
800
Figure 11: Visualization token-wiss wise activations in VAR-d16. Data come from a single sample.
Table 10 shows the performance of PTQ4ARVG on PAR-3B-4x and PAR-3B-16X.
While our approach demonstrates clear superiority at 6-bit, its 8-bit performance is par with OmniQuant and SVDQuant for some models. To emphasize the advantages of our method, we include additional W4A8 experiments, with Table further illustrating its superiority.
K COMPARISON WITH QUAROT
In some LLMs, the operation between two adjacent blocks is of the form XW, where X denotes the output of the previous block and denotes the weights the next block. When the two blocks are connected only by normal LayerNorm (e.g.. RMSNorm LN), the rotation matrix Q can be used offline, because of:
LayerNorm X) LayerNorm(XQQ" Q
(40)
However, in VAR and RAR, the blocks are not only connected by normal LayerNorm, but also by a specialized LayerNorm, AdaLN. AdaLN transforms the condilioning input into modulation fac- tors (MHSAscate1, MHSA MHSAxcate2, FFN FFN FFNahift1. ftl FFN scaac RT*)), which adjust the distribution activations. As illustrated in Fig. assuming the MHSA output is denoted X and the residual X,, the computation of FFN.fcl with weight W is formulated as:
output (LN(X- MHSA + X,) FFN + FFNstiff)) W
(41)
please refer to the VAR and RAR code for detail. As shown. applying rotation matrices offline does not preserve computational equivalence. Therefore, for VAR and RAR, rotation should be applied
20

---

Published as a conference paper at ICLR 2026
rar.block.O.adk
rar.block.5aad
rar.block. 11 11.proj
rar. blo.k.oo...333 (0ockk23..rr)
Dooddd
0.0-
-0.5
100
150
100
150
700
250
250
16
60
0.5
0.5
8-
10
-15
-1.0
1.00
100
350
250
-10
-00
1.2
class 2 booand
-0.3
0..5
50
100
150
200
25.0
50
100
150
200
250
100
150
200
250
50
100
150
200
250
0.0
0.0
class
-0.3
20
100
350
200
250
unsond
0.5
10
-0.5
-15
-1.0
Figure 12: Visualization of token-wise activations RAR-B. Data come from single sample.
dynamically. The DiT model also employs AdaLN and shares a similar block structure with VAR and RAR. In ViDiT-Q, rotation matrices are also introduced to suppress outliers (although not explicitly mentioned in the paper). These rotation matrices are likewise applied dynamically, as shown in the ViDiT-Q code.
As result, all Hadamard matrices involved in QuaRot need to computed online when applied to RAR and VAR. This not only introduces significant overhead during inference but also increases peak memory usage. As reported Table 12, with batch size of 100 and a token sequence length of 256, QuaRot even leads to 0.70x slowdown and 159MB increase in peak memory usage at 8-bit quantization. Conversely, PTQ4ARVG achieves superior accuracy and quantization efficiency compared to QuaRot.
L LIMITATIONS
Recently, ARVG models demonstrates superior image generation capabilities compared to diffusion models. More importantly, their LLMs--ompatible architecture and strong scaling laws make them a current focus of research. However, its deployment with the quantization techniques still remains largely unexplored. To address this gap, we propose PTQ4ARVG, an accurate and efficient post- training quantization framework tailored for the ARVG family.
Although our method can effectively quantize the weights and activations of ARVG models into 8-bit and 6-bit while preserving competitive performance, it struggles to maintain such high level of
21

---

Published as a conference paper at ICLR 2026
Table 9: Comparative results for RAR and VAR models.
RAR-I
VAR-d20
Bit Width
Methods
IS
FID
sFID
Precision
IS
FID
sFID +
Precision
303.00
1.76
309.09
2.85
7.66
0.83
SmoothQuant
245.99
2.73
7.29
0.76
260.02
3.50
12.51
0.81
RepQ*
253.16
2.55
7.34
0.76
258.90
3.46
11.98
0.79
OS+
252.98
2.70
7.94
0.67
260.96
3,40
11.25
0.81
W8A8
OmniQuant
289.61
2.16
6.66
0.79
239.30
4.14
11.73
0.77
QuaRot
271.37
2.35
7.42
0.79
260.52
4.02
11.20
0.81
SVDQuant
284.59
1.95
6.38
0.80
252.17
3.80
11.87
0.81
Ours
291.55
1.90
6,34
0.81
263.86
3.36
11.17
0.82
SmoothQuant
31.97
63,34
40.03
0.39
190.96
5,21
10.61
0.71
RepQ*
23.32
76.95
47.04
0.32
146.00
7.96
11.84
0.68
OS+
20.09
88,65
38.70
0.25
185,95
5,00
11.93
0.73
W6A6
OmniQuant
130.84
17.80
17.39
0.61
113.66
16.78
14.18
0.59
QuaRot
45.27
53.27
34.09
0.45
190.73
5.02
10.68
0.74
SVDQuant
200.65
5.28
8.06
0.71
151.88
7.50
11.47
0.64
Ours
219.40
3,99
8.14
0.75
194.85
4.82
10.47
0.75
Bit Width
Methods
RAR-XXL
VAR-d30
FP
328.87
1.51
5.13
0.81
307.24
2.03
8.72
0.81
SmoothQuant
278.89
2.09
5.91
0.77
247.74
4.37
18.19
0.76
RepQ*
233.35
3.25
6.45
0.77
262.34
3.51
16.05
0.79
OS+
245.23
2.67
6.23
0.77
269.86
3.30
15.89
0.81
W8A8
OmniQuant
288.10
2.35
6.43
0.77
268.92
3.35
15.34
0.81
SVDQuant
276.52
2.14
5,46
0.75
268.55
3.34
14.87
0.80
Ours
321.03
1.61
5.28
0.82
277.05
3.27
14.40
0.81
SmoothQuant
164.15
12.47
18.74
0.67
101.01
22.67
32.05
0.59
RepQ*
83.01
28.77
23.63
0.56
113.37
17.02
25.18
0.62
OS+
66.82
34,80
16.29
0.47
156.21
11.15
24.45
0.68
W6A6
OmniQuant
184.25
10.89
131.52
14.95
0.65
SVDQuant
188.43
7.06
160.53
10.85
0.70
Ours
266.39
2.41
168.84
8.50
21.38
Table 10: Comparative results for PAR models.
PAR-3B-4x
PAR-3B-16X
Bit Width
Methods
IS
FID
sFID
Precision
18
FID
sFID
Precision
FP
255.5
2.29
0.82
262.5
2.88
0.82
SmoothQuant
10,91
112.24
40.94
0.10
12.00
79.45
78.58
0.16
RepQ
5.08
168.03
88. 96
0.06
11.74
79.34
70.16
0.16
OS+
167.17
77.37
0.06
11.71
79.84
75.06
0.16
W8A8
OmniQuant
200.09
3.50
5.43
0.76
211.98
4.11
7.54
0.75
SVDQuant
202.16
3.60
0.76
210.85
4.14
7.55
0.73
Ours
200.42
3.57
0.76
207.48
4.16
0.73
SmoothQuant
137.03
42.37
0.06
9.49
107.07
96.20
0.11
RepQ
5.03
166.43
105.89
0.10
11.24
89.81
69.18
0.11
OS+
157.67
73.02
0.06
9.37
109.55
102.72
0.11
OmniQuant
16.58
113.29
60.21
0.20
17.89
102.87
75.35
0.21
W6A6
SVDQuant
54.59
41.26
29.97
0.49
28.31
71.22
61.22
0.29
SQ+STWQ
96.69
15.55
7.06
0.58
71.39
23.41
12.08
0.44
RepQ*+STWQ
51.15
31.83
15.09
0.44
92.32
18.65
11.30
0.55
Ours
121.91
10.05
6.22
0.63
107.52
15.47
10.72
0.58
accuracy when quantizing the model to 4-bit. Despite the limitations, we hope that our work could inspire the research interest on ARVG quantization within the community. We also further enhance our method to achieve better performance under lower bit-precision.
M ETHICS STATEMENT
This work proposes quantization-baser acceleration methods for autoregressive visual generation models. relies solely on publicly available datasets and does not involve human subjects, private data, personally identifiable information. The methods designed to improve computational efficiency and reduce energy consumption, thereby lowering the environmental cost model deployment. We believe the ethical risks of this research minimal.
22

---

Published as a conference paper at ICLR 2026
Table 11: Comparative results W4A8 precision.
Bit Width
Model
Method
18
FID +
sFIl
Precision
OmniQuant
20.28
58.84
27.69
0.33
VAR-d16
SVDQuant
88.34
19.26
22.50
0.57
Ours
114.09
17.06
16,99
0.58
OmniQuant
125.77
15.86
16.13
0.59
W4A8
RAR-B
SVDQuant
11.92
134.65
43,20
0.15
Ours
158.09
10.75
14.95
0.63
OunniQuant
7,40
133.55
43.76
0.13
PAR-XL
SVDQuant
95.23
19.81
18.19
0.52
Ours
110.97
15.49
9.36
0.55
Table 12: Comparison with QuaRot on RAR-L with 8-bit quantization.
Method
1St
FID
Precision
Time (ms)
Memory (MB)
Speedup
FP
303.00
1,76
0.81
3722
4241
1.00x
QuaRot
271.37
2.35
0.79
6062
2345
0.70x
PTQ4ARVG
291.55
1.90
0.81
1297
2186
2.87
N REPRODUCIBILITY STATEMENT
We have made every effort ensure the reproducibility of our work. The paper provides detailed descriptions of the proposed methods, experimental setups, and evaluation protocols. Additionally, we provide complete source code and instructions in the supplementary materials.
O RANDOM SAMPLES
OS+
BrDQuam
Ours
Figure 13: Random samples of PAR-XL with 8-bit quantization.
23

---

Published as a conference paper at ICLR 2026
OS+
SVDQuant
Ours
Figure 14: Random samples of MAR-B with 8-bit quantization.
FP
SQ
RepQ
OS+
OmniQuant
QuaRot
Ours
Figure 15: Random samples of RAR-B with 6-bit quantization.
24

---

Published as a conference paper at ICLR 2026
SQ.
OS+
QuaRot OmniQuant
Ours
Figure 16: Random samples of RAR-XL with 6-bit quantization.
FP
SQ
RepQ
OS+
QuaRot
Ours
Figure 17: Random samples of VAR-d16 with 6-bit quantization.
25


Reviews:
[{"review_id": "QDMvS4TDEw", "reviewer": "Reviewer_D8Am", "summary": "This paper proposes several techniques to improve the quantization of autoregressive visual generation (ARVG) models. The authors identify that a key challenge in quantizing ARVG lies in its highly skewed and dynamic activation distributions. To address this, they introduce a Gain-Projected Scaling (GPS) mechanism that stabilizes quantization and better preserves generative performance. Experimental results demonstrate substantial improvements over existing quantization baselines across multiple ARVG architectures.", "questions": "Please refer to the weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "* The proposed Gain-Projected Scaling (GPS) offers a principled mechanism for balancing weight and activation scaling by leveraging Hessian information rather than relying on empirical heuristics. This theoretically grounded design suggests strong potential for broader applicability beyond ARVG models.\n\n* Experimental results consistently show that the proposed method achieves significant improvements over baseline quantization approaches, underscoring its effectiveness and practical value.", "weaknesses": "* Achieving the optimal GPS configuration appears challenging compared to empirical estimation. The approach involves several approximations, including those for the Hessian matrix, the upper bound of overall quantization loss, and scaling quantization error. These approximations may introduce discrepancies across different input distributions and norms. Moreover, the final GPS algorithm computes scaling based only on the most significant channel, which may not reflect a global—or even local—optimum. It would be helpful if the authors could include ablation studies illustrating how quantization error changes with GPS.\n\n* The methodological presentation lacks clarity, particularly in Section 4.3, which is relatively brief and insufficiently detailed. The notion of mismatched calibration requires a clearer explanation, as does the proposed distribution-guided calibration and how it specifically resolves this issue.\n\n* The paper does not clearly articulate the unique challenges of quantizing ARVG models. Dynamic ranges and channel-wise outliers are common issues in diffusion model quantization, and the proposed GPS appears to be a general technique rather than one specifically tailored for ARVG. Similarly, the STWQ module seems more like a heuristic quantizer selection strategy based on data distribution. The authors should clarify what makes ARVG quantization distinct and how their method specifically addresses those challenges.\n\n* Empirically, the method shows only marginal improvements except on VAR and PAR models. It would be helpful to clarify whether this is related to baseline implementation or model-specific characteristics.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7RsoFs9S3F", "reviewer": "Reviewer_oG1L", "summary": "This paper studies PTQ for ARVG models. The authors empirically identify three categories of quantization challenges for ARVG: severe channel-level outliers, highly dynamic activations along the token dimension, and sample-level distribution mismatch. To address these, they propose the PTQ4ARVG framework, which comprises three components, GPS, STWQ, and DGC. Experiments cover multiple ARVG models (VAR, RAR, PAR, MAR), and comparisons under W8A8 and W6A6 settings against baselines such as SmoothQuant, OS+, OmniQuant, QuaRot, and SVDQuant show advantages in generation quality and inference acceleration.", "questions": "1. How large is the effect of omitting Hessian cross terms and assuming Hessian constancy in GPS on the final scaling factors? Can you provide real Hessian statistics or approximations (diagonal, top-k) for several representative layers, such as MHSA, FFN, and projection layers, to validate the second-order Taylor approximation and the omission of cross terms? If the assumptions are invalid, by how much does GPS’s closed-form solution deviate?\n2. Does STWQ’s \"position-invariant distribution\" hold across different models, different conditioning modes (conditional vs unconditional), and different datasets (ImageNet vs more complex scenes)? I suggest providing cross-model and cross-dataset statistics, or at least verifying position distribution stability on the classification/conditioning subsets of PAR, MAR, and VAR.\n3. Please provide full reproduction details for baseline implementations and comparisons, including the specific implementation versions, calibration sample counts used for each method, whether KV-cache / embeddings / layernorm parameters were quantized, and whether custom CUDA kernels were used. If possible, include key reproduction scripts or hyperparameter tables in an appendix.\n4. For example, in Table 1 the VAR-d16 W8A8 IS has already dropped by nearly 20%, while historically 8-bit quantization is often considered effectively lossless, Why then does the paper still claim maintained competitive performance in the abstract? Please clarify and reconcile these claims with the observed degradations.\n5. GPS numerical stability, Formula (16) includes square roots and denominators, In channels with sparsity or where Δx approaches zero, might this lead to numerical instability? What regularization or clipping strategies are used to prevent division by near-zero values?\n6. STWQ vs DTWQ trade-off, STWQ avoids online overhead but how does it handle variable sequence lengths or deployment scenarios requiring arbitrary cropping? Do you need to rebuild static tables for different sequence lengths, or is there an adaptive strategy?\n7. DGC’s Mahalanobis thresholding, the paper fixes “top 50%” of samples as calibration picks, how was this threshold chosen? Does it need tuning across models or datasets, and is there a more automatic threshold selection method, for example based on distributional entropy elbow points?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Problem formulation is clear and well targeted, The authors carefully analyze how ARVG differs from standard LLMs and diffusion models in activation distributions and token structure, The three proposed challenges at the channel, token, and sample levels capture the essential difficulties of quantizing ARVG, providing a solid basis for method design.\n2. Method design has theoretical support, The GPS component is not purely heuristic, it derives an analytic expression for the effect of the scaling factor via decomposition of the quantization loss and a Taylor expansion, and it yields a closed form or solvable expression. This is more convincing than many scaling methods that rely only on heuristic statistics.\n3. Practicality and deployment considerations are thorough, The STWQ staticization idea matches ARVG’s fixed sequence length property, avoiding dynamic online calibration overhead, and the authors demonstrate deployment with standard CUDA kernels and real latency and memory measurements, which strengthens the engineering credibility of the work.\n4. Comprehensive experimental coverage and ablation, The paper compares several mainstream ARVG models across multiple bit widths (8/6 bit), and provides component-wise ablations for GPS, STWQ, and DGC, showing each component’s contribution to overall performance, The empirical comparison is relatively systematic.", "weaknesses": "1. Several important approximations and assumptions in GPS are not sufficiently validated, GPS omits Hessian cross terms in its derivation, however prior series of quantization works (OBD[1], OBS[2], OBC[3], GPTQ[4]) have pointed out that such omissions can introduce significant errors.\n2. The position-invariance assumption underlying STWQ is limited, The authors rely on “position-invariant distributions” as the core justification for STWQ, but the paper only shows statistics for a few layers and a few sample types (Fig.4), There is insufficient validation across models, classes, or conditioning states, If some classes or conditioning strongly affect position distributions, STWQ’s effectiveness may degrade.\n3. Baseline comparisons and fairness of hyperparameter / implementation choices are unclear, Several baselines compared (notably OmniQuant, QuaRot, SVDQuant) have multiple implementations and hyperparameter variants in the literature, The paper states “default settings” but does not specify reproduction details, calibration sample counts, or whether fine-tuning was permitted, Some baselines show anomalously poor performance in the tables, which could indicate inconsistent setups or implementation issues, this undermines confidence in the conclusions.\n4. Some result presentations and tables lack readability and consistency, Tables 1/2/3 contain many anomalous or extreme values (for example SVDQuant’s IS collapse under W8A8 in Table 1), but the paper does not explain or annotate these anomalies, Table headers and notes are insufficiently detailed, making it hard to trace exact experimental settings such as batch size, calibration sample count, sequence length, and whether embedding / KV-cache were quantized.\n5. The method already fails to preserve accuracy compared to full precision at 8 bits in most settings, this greatly affects practicality, The paper also does not explore more aggressive quantization at 4 bits or below.\n\nReferences:\n\n1. LeCun et al., Optimal Brain Damage.\n2. Hassibi et al., Optimal Brain Surgeon and general network pruning.\n3. Elias et al., Optimal Brain Compression.\n4. Frantar et al., GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "B1SjQrm8BD", "reviewer": "Reviewer_x7xs", "summary": "PTQ4ARVG addresses the underexplored challenge of applying Post-Training Quantization (PTQ) to Autoregressive Visual Generation (ARVG) models , which share architectural similarities with large language models but achieve visual performance comparable to diffusion-based models. The paper identifies three key obstacles specific to ARVG models that cause conventional PTQ methods to fail: (1) severe outliers at the channel-wise level (due to AdaLN-adjusted activations), (2) highly dynamic activations at the token-wise level (due to positional embedding and sink tokens), and (3) mismatched distribution information at the sample-wise level. To overcome these, the authors propose a tailored PTQ framework, PTQ4ARVG, which consists of Gain-Projected Scaling (GPS), Static Token-Wise Quantization (STWQ), and Distribution-Guided Calibration (DGC). The ultimate goal is to significantly reduce model size and inference latency, enabling efficient deployment of large ARVG models on resource-constrained devices without the need for extensive retraining.", "questions": "None", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. High Relevance and Motivation: The work addresses a critical bottleneck—quantization—for deploying large generative models, filling a significant research gap within the emerging ARVG model class.\n\n2. Clarity and Technical Detail: The paper clearly articulates the unique quantization challenges specific to ARVG and proposes technically sound solutions, such as GPS, which optimizes the scaling factor using a closed-form solution derived from a Taylor series expansion.\n\n3. Extensive Robustness Validation: The method is evaluated across a broad family of state-of-the-art ARVG models (VAR, RAR, PAR, MAR), demonstrating consistent superiority over existing PTQ methods at both W8A8 and W6A6 bit-widths.", "weaknesses": "The generative evaluation is confined to the ImageNet dataset and relies heavily on traditional metrics like FID/IS which correlate poorly with human perception, suggesting that the inclusion of modern perceptual metrics (e.g., HPS [1] or CLIP Score) on diverse, high-fidelity datasets is strongly recommended for a more robust comparison.\n\n[1] Ma Y, Wu X, Sun K, et al. Hpsv3: Towards wide-spectrum human preference score. CVPR 2025: 15086-15095.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pj7NRFCmIs", "reviewer": "Reviewer_FANZ", "summary": "This paper is the first to systematically investigate the problem of post-training quantization (PTQ) for Autoregressive Visual Generation (ARVG) models. The authors identify three key challenges when directly applying existing PTQ methods to ARVG models: (1) severe channel-wise outliers, (2) highly dynamic token-wise activations, and (3) mismatched sample-wise distribution information. To address these challenges, the authors propose PTQ4ARVG, a training-free PTQ framework consisting of three core components: (1) Gain-Projected Scaling (GPS), which theoretically derives an optimal scaling factor to mitigate outliers by expanding the quantization loss via a Taylor series; (2) Static Token-Wise Quantization (STWQ), which leverages the fixed token length and position-invariant activation distribution of ARVG models to achieve fine-grained quantization without runtime overhead; and (3) Distribution-Guided Calibration (DGC), which selects more informative calibration samples by maximizing distributional entropy. Extensive experiments show that PTQ4ARVG can effectively quantize various ARVG models (e.g., VAR, RAR, PAR, MAR) to 8-bit and 6-bit, outperforming existing PTQ methods.", "questions": "1. Regarding the GPS Assumption: In the derivation of GPS, you assume that the Hessian of the loss function with respect to the layer's output can be treated as a common constant and thus ignored during optimization. This is a strong simplification. Could you please elaborate on the validity of this assumption? Have you analyzed the actual variation of this Hessian value in ARVG models? How sensitive is the performance of GPS to this assumption? \n2. Pareto Front Analysis vs. Smaller Full-Precision Models: As this is a pioneering work in PTQ for ARVG models, it is crucial to establish the practical benefits of quantization against the primary alternative for efficiency: training smaller, full-precision models. Does a quantized large model (e.g., a 6-bit RAR-XL) offer a better trade-off than a smaller full-precision model (e.g., a FP RAR-B) with a similar FLOPs count? A comparison on a FLOPs-vs-FID plot would be highly valuable to demonstrate that the proposed quantization method truly pushes the Pareto front, rather than just landing on a point that could be achieved by a smaller, unquantized model. \n3. On the Composability of the Methods: The proposed methods are designed to tackle specific challenges in ARVG. We are interested in understanding the composability of these methods with other advanced PTQ techniques, such as low-rank decomposition from SVDQuant or rotation-based methods from QuaRot. For instance, could combining GPS with these approaches lead to further performance gains, potentially achieving near-lossless results at 6-bit and 8-bit? This is crucial for assessing the generality and future potential of your framework\n$\\textbf{If these issues can be resolved, I will consider giving a higher score.}$", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Pioneering and Important: This paper is the first comprehensive attempt to tackle the emerging and important problem of quantizing ARVG models, a largely unexplored area. With models like VAR and MAR gaining prominence, this work is very timely\n2. Deep Problem Insight: The paper does not merely apply existing PTQ methods to a new model class. Instead, it deeply analyzes and identifies three specific, critical challenges (channel outliers, token dynamics, sample mismatch), strongly supporting these observations with visual evidence (Fig. 1). This in-depth analysis is a key strength\n3. Solid Theoretical and Methodological Innovation: To address channel outliers, the proposed GPS method is not just empirical; it is mathematically modeled via a Taylor expansion of the quantization loss to derive a closed-form solution for the optimal scaling factor (Eq. 16). This is a solid and elegant theoretical contribution. Furthermore, STWQ is a clever solution that leverages the unique properties of ARVG models (\"fixed token length\" and \"position-invariant distribution across samples\") to solve the dynamic activation problem with a static, zero-overhead approach\n4. Comprehensive Experimental Validation: The authors conduct extensive experiments on four different ARVG models (VAR, RAR, PAR, MAR) and compare against a wide range of state-of-the-art PTQ methods. The thorough ablation studies (Table 4, 5, 6) also clearly demonstrate the effectiveness of each proposed component", "weaknesses": "1. Performance Not Yet Optimal, Some 8-bit Results Are Not Near-Lossless: Although the proposed method outperforms other PTQ baselines, there is still room for performance improvement. For many quantization applications, 8-bit (W8A8) PTQ is often expected to achieve near-lossless performance compared to the full-precision (FP) model. However, the experimental results show that several models still exhibit a noticeable performance drop even at 8-bit. For instance, on VAR-d24, the FID degrades from 2.33 to 3.36, and on PAR-XL, it degrades from 2.61 to 3.55 (Table 1, Table 2). This suggests that ARVG models may be inherently more sensitive to quantization than other architectures, and achieving truly lossless performance remains a challenge even at 8-bit. This issue is, as expected, exacerbated at the more aggressive 6-bit setting, where the performance drop becomes much more significant (e.g., FID on RAR-B drops from 1.96 to 5.13).\n 2. Absence of Comparison with QAT: The paper focuses exclusively on PTQ. While PTQ is valuable for its efficiency and training-free nature, providing a comparison with Quantization-Aware Training (QAT) would offer a more complete picture of the performance-cost trade-off. Even a simple QAT baseline (or even PEFT like Qlora) would help contextualize the performance level achieved by PTQ4ARVG.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VufB7H5h18", "reviewer": "Reviewer_NhoA", "summary": "The paper introduces PTQ4ARVG, the first post-training quantization framework for autoregressive visual generation (ARVG) models. It tackles channel outliers, token-wise dynamics, and sample-wise mismatches with Gain-Projected Scaling, Static Token-Wise Quantization, and Distribution-Guided Calibration, achieving strong 6/8-bit results and real GPU speedup over prior PTQ methods.", "questions": "Please refer to the weakness section.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. Novel problem setting, first systematic study of PTQ for ARVG models.\n2. Clear motivation with three unique quantization challenges.\n3. GPS provides a theory-driven scaling solution rather than heuristics.\n4. STWQ and DGC are training-free and hardware-friendly.", "weaknesses": "1. Section 4.2 and 4.3 are relatively short and underdeveloped, lacking detailed analysis.\n2. No visual comparison results are provided to qualitatively validate generation quality.\n3. 8-bit results still show notable degradation, raising concerns about practical usability.\n4. The reported 3.01× speedup at 8-bit is questionable without more deployment details.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)