ERTACache: Error Rectification and Timesteps Adjustment for Efficient Diffusion
Authors: Xurui Peng
Chenqian Yan
Hong Liu
Rui Ma
Fangmin Chen
XING WANG
Zhihua Wu
Songwei Liu
Mingbao Lin
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion
Primary area: generative models

Abstract: Diffusion models suffer from substantial computational overhead due to their inherently iterative inference process. While feature caching offers a promising acceleration strategy by reusing intermediate outputs across timesteps, naive reuse often incurs noticeable quality degradation. 
In this work, we formally analyze the cumulative error introduced by caching and decompose it into two principal components: feature shift error, caused by inaccuracies in cached outputs, and step amplification error, which arises from error propagation under fixed timestep schedules.
To address these issues, we propose ERTACache a principled caching framework that jointly rectifies both error types. Our method employs an offline residual profiling stage to identify reusable steps, dynamically adjusts integration intervals via a trajectory-aware correction coefficient, and analytically approximates cache-induced errors through a closed-form residual linearization model. Together, these components enable accurate and efficient sampling under aggressive cache reuse.
Extensive experiments across standard image and video generation benchmarks show that ERTACache achieves up to 2x inference speedup while consistently preserving or even improving visual quality. Notably, on the state-of-the-art Wan 2.1 video diffusion model, ERTACache delivers 2x acceleration with minimal VBench degradation, effectively maintaining baseline fidelity while significantly improving efficiency.

Published as a conference paper at ICLR 2026
ERTACACHE: ERROR RECTIFICATION AND TIMESTEPS ADJUSTMENT FOR EFFICIENT DIFFUSION
Xurui Peng': Chenqian Yan' Hong Liu'; Rui Ma', Fangmin Chen', Xing Wang', Zhihua Wu', Songwei Liu Mingbao Lin?t ByteDDnce Inc. Rakktten Asia
ABSTRACT
Diffusion models suffer from substantial computational overhead due to their in- herently iterative inference process. While feature caching offers promising ac- celeration strategy by reusing intermediate outputs across timesteps, naive reuse often incurs noticeable quality degradation. In this work, we formally analyze the cumulative error introduced by caching and decompose it into two principal components: feature shift error, caused by inaccuracies in cached outputs, and step amplification error, which arises from error propagation under fixed timestep schedules. To address these issues, we propose ERTACache, principled eaching framework that jointly rectifies both error types. Our method employs an offline residual profiling stage to identify reusable steps, dynamically adjusts integration intervals via rajectory-awan correction coefficient, and analytically approxi- mates tache-induced errors through a closed-form residual linearization model. Together, these components enable accurate and efficient sampling under aggres- sive cache reuse. Extensive experiments across standard image and video gen- eration benchmarks show that ERTACache achieves up to 2x inference speedup while consistently preserving or even improving visual quality. Notably, on the state-of-the the-art Wan2.1 video diffusion model, ERTACache delivers 2x acceler- ation with minimal VBench degradation, effectively maintaining baseline fidelity while significantly improving efficiency.
INTRODUCTION
Diffusion models Ho et al. 2020); Song et al. (2020); Rombach et al. (2022): Labs 2024) have
demonstrated remarkable capabilities. driving groundbreaking progress across diverse domains such as image Esser et al. 2024), video Wan et al. (2025). 3D content Kong et al. (022): Huang et al.) (2024a), and audio generation Schneider et al. (202)), largely owing to the scalability transformer- based architectures Vaswani et al. (2017). To further improve generation quality, researchers have continually scaled up Diffusion Transformers (DiTs) Peebles & Xie (2023). However, this comes at the cost of significantly increased inference time and memory consumption cinipttton-iiuusss iissuss that par- ticularly pronounced video generation, where synthesizing a 6-second 480p clip on an NVIDIA 1.20 GPU take 2-5 minutes, posing serious limitations for practical deployment. To address these deficiencies, a variety of acceleration techniques have been proposed, including reduced-step samplers] Lu et al. (2022. [iu al. (202). model distillation Zhou et al. (2024). quantization strate- gies
Li et al. (2024), and cache-based accelcration Liu et al. 2025b a).
Among various acceleration strategies, cache-based feature reuse has emerged as a particularly prac- tical solution. By reusing intermediate model outputs across timesteps, this approach well reduces redundant computations during inference, yielding substantial speedups without requiring additional training overhead. Moreover, it generalizes readily to diverse video diffusion models, making it appealing for real-world deployment. However, existing cache-based methods still face key limita- tions. Approaches that cache internal transformer states Kahatapitiya et al. (2024); Ji et al. (2025) often incur high GPU memory costs. Meanwhile, methods like TeaCache Liu et al. (2025b), which
"Equal Contribution.
1 Cooresonniing Authors.
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Published as a conference paper at ICLR 2026
(a) Offiine Policy Callbration
(b) Timestep Adjustment
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Figure 1: Framework of our proposed ERTACache.
choose dynamically predict cache strategies based on input-dependent heuristics, can exhibit dis- crepancies between predicted reuse quality and actual reconstruction error, limiting reliability.
In this work, we begin with a systematic analysis dynamic cache-based acceleration. Our empiri- cal findings reveal that despite the input-dependent nature of diffusion trajectories, cache reuse pat- terns and associated errors exhibit strong consistency across prompts. This suggests that generic, offline-optmiized caching strategy may be sufficient to approximate optimal behavior in most sce- narios. Furthermore, we dive into insights by decomposing the sources of cache-induced degradation and identifying two dominant error modes: (i) feature shift error, introduced by inaccuracies in the reused model outputs; and (ii) step amplification error, which arises from the temporal compounding of small errors due to fixed timestep schedules.
Motivated by the above insights, we formally introduce ERTACache, a principled framework for error-aware caching in diffusion models. Figure[] overview of our ERTACache framework. Our proposed ERTACache adopts a dual-dimensional correction strategy: (1) we first perform of- fline policy calibration by searching for globally effective cache schedule using residual error profiling; (2) we then introduce a trajectory-aware timestep adjustment mechanism to mitigate in- tegration drift caused by reused features; (3) finally, we propose an explicit error rectification that analytically approximates and rectifies the additive error introduced by cached outputs, enabling accurate reconstruction with negligible overhead.
Together, these components enable ERTACache to deliver high-quality generations while substan- tially reducing compute. Notably, our proposed ERTACache achieves over 50% GPU computa- tion reduction video diffusion models, with visual fidelity nearly indistinguishable from full- computation baselines.
Our main contributions can be summarized as follows:
We provide formal decomposition of cache-induced errors in diffusion models, identify- ing two key sources: feature shift and step amplification.
We propose ERTACache, a caching framework that integrates offline-optimized caching policies, timestep corrections, and closed-form residual rectification.
Extensive experiments demonstrate that ERTACache consistently achieves over 2x in- ference speedup state-of-the-ard video diffusion models such Open-Sora 1.2, Cog VideoX, and 1a with significantly better visual fidelity compared to prior caching methods.
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RELATED WORK
Diffusion-based Generation Early diffusion models such as DDPM Ho et al. (2020) produce high-resolution images through 1000-step Markov denoising chain, but their sheer number of iter- ations incurs prohibitive latency. DDIM Song et al. (2020) later shortened this chain to 50-100 implicit steps, yet even these "fast" samplers remain slow for time-critical applications. More recent systems- e.g., Stable Diffusion Esser et al. (2024) with architectural streamlining and Flux-dev1. Labs (2024) with simplified Transformer
the cost per step, but cannot escape the fundamental requirement of many denoising passes to preserve fidelity. Video genera- tion magnifies the problem: adding a temporal dimension and higher spatial resolutions multiplies compute.
CogVideoX Yang et al. (2024), despite block-causal attention and a 3D-VAF compressor, still needs 113s (50 steps) to render 480p six-frame clip on single H800 GPU. OpenSora 1.2 (Zheng et al. (2024) accelerates per-frame frame inference via a
design and dynamic sparse attention, while Hunyuan Video Kong et al. (2024) achieves 1.75x speed-up with sliding-window attention and DraftAttention Shen et al. (2025a). WAN-2.1 Wan et al. (2025). which prioritizes temporal coher- ence, pushes latency even higher 200hsr--00s for 81-frame 480p clip an A800
the gap to real-time use. The root cause is intrinsic: every denoising step forwards a large UNet Ron- meberger al. (2015) or Transformer whose parameter count scales roughly quadratically with input size. Consequently, despite impressive image and video quality, diffusion models remain hamstrung by extreme inference times, especially in the video domain.
Cache-based Acceleration Because iterative denoising dominates runtime, feature caching has emerged promising accelerator. The idea is to exploit feature similarity across successive steps by storing and reusing intermediate activations. TeaCache Liu et al. (2025b) predicts layer outputs from inputs to decide what cache, although its learned policy often collapses to a fixed sched- ule. Finer-gu tieee-ggrindd schemes such as AdaCache Kahatapitiya et al. (024), BAC Ji et al. (2025), ) and DiTFastAttn Yuan et (2024) cache blocks or attention maps, but their granularity causes memory to grow quadratically, leading to out-of-memory failures on long videos. Token-level approaches- including FastCache Liu et al. (2025a), TokenCache Lou et al. (0224), and Duca Zou et al. (2024)- are orthogonal to our method and complementary in principle. LazyDiT[Shen et al. (2025b) imposes extra training losses to regularize cached features, incurring additional training overhead, whereas strategy achieves quality retention via direct error compensation with no retraining. ICC let al. (2025) adjusts cached features through low-rank calibration; in contrast, we explicitly model the residual error, ensuring both accuracy and memory efficiency during generation.
3 METHODOLOGY
3.1 PRELIMINARIES
Diffusion Process Diffusion models generate data by gradually perturbing a real sample z0 Pdota into pure Gaussian noise JT ~ (0, 1) and then inverting that transformation. Recent work- most notably Stable Diffusion (SD3) Esser et al. (2024) and the Flow-Matching paradigm Lipman et al. (2022) has positioned dow-based objectives as the dominant framework for both image and video synthesis. Under flow matching, the forward process is a simple linear interpolation:
Ø₁ (1 t)æo + ETT, t [0.1].
(1)
whose trajectory has constant true velocity field: dr
Ue dt
xT To-
(2)
A neural estimator by (ze, f) is trained to predict this velocity by minimizing the mean equanee error: (fM( = Et [xlea t) - -411
(3)
During synthesis, one deterministically integrates the learned ODE from noise back to data. Dis- cretizing the time axis for i T 1,... 0 yields:
1 xz Ad, vo (),
(4)
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Published as a conference paper at ICLR 2026
where At, denotes the interval between two consecutive time steps. As i O, the sample z0 converges to the data distribution Pdata.
Residual Cache Inference latency is dominated by repeated evaluations of the denoising network across many time steps. Residual caching tackles this bottleneck by exploiting the strong feature locality between successive steps: instead of storing full activations, cache only the residual that the network adds to its input, preserving maximal information with minimal memory. Following prior work Chen et al. 2024); Liu et al. (222b)), record at step 1: ()) 2++1-
(5)
step i skipped, the model output reconstructed on-the-fly as following:
i = I; + +r,
(6)
thereby avoiding full forward pass while retaining high-fidelity features for the subsequent update.
3.2 ANALYZING ERROR ACCUMULATION FROM FEATURE CACHIIN
To understand the trade-offss in the cache-based based acceleration scheme, we analyze how approximating model outputs with cached features introduces and accumulates error during the diffusion trajectory.
Let 8 denote the cached model output step i, approximating the true velocity e us (2(2,, t). This approximation introduces an additive error term E;:
i 0; Eg.
Replacing V:41 with ú in Eq. yields the cached latent:
T 1i+1 A4++1 0z+1-
(8)
Then, the deviation from the true trajectory after a single step is derived in the following:
6; = E = At;41 Ei+1-
(9)
If the caching operation is performed for m continuous steps right after the i-th step (m 2 1) using Euler method, meaning that cached vectors replace actual computations during the to i m interval, then we have:
f = - + A4im++1 00 0
m+1
(10)
and the trajectory deviation 8,mm compounds:
8p-m = Fm-m - 8im-m
f;-m+1 mi-m+11
+ Ati-m+1 ū--m11 At--m+1 Ui-m-1
6t-m41 + Ats-m+1 Et-m+1
(11)
k=
Step Amplification Errorr
Emor
This reveals two critical sources of error in cache-based acceleration: 1) Feature Shift Error: The discrepancy between eached features and ground-trut truth computations causes deviation ODE trajectories, 2) Step Amplification Error: The pre-existing feature shift gets compounded through temporal integration, with error magnification.
3.3 ERTACACHE
Recent work such as TeaCache Liu et al. (2025b) has explored the potential of cache-based accel- cration diffusion sampling by predicting timestep-speeific reuse strategies via online heuristics. Although effective to a certain extent, these methods still rely heavily on threshold tuning and of- ten fail to generalize across different prompts or sampling trajectories. Motivated by our analysis that reveals consistent eиrо patterns across diverse inputs (see Fag,re we propose principled framework that combines offline policy calibration, adaptive timestep adjustment, and explicit enror rectification to achieve more principled and reliable caching for diffusion models.
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Published as a conference paper at ICLR 2026
0.25
Open-Sees
2.15
Trrst mototer D.Cudee cnjorie
Livaad--nutt
TeaCadhe Proili Li Distace
9.03
15
25
Steps
(a) Ground-trute vs. TeaCache's & distance across timesteps
(b) ODE trajectories with/without timestep ad- justment
Figure& 2: (a) The ground-truth & distance (blue) between real cached and computed features shows minor variation across timesteps. In contrast, Tea-Cache's predicted & distance (orange) remains consistent across prompts but diverges significantly from ground-truth in later steps, indicating growing prediction error over time. (b) ODE trajectories with and without timestep adjustment.
Offiine Policy Calibration via Residual Error Profiling While previous approaches Liu et al. (2025)) predict caching decision on-the-fly, we instead formulate caching as an offline optimization problem. Let r(x;, i) denote the residual computed during standard inference. When caching is introduced, the residual to be reused is represented as F. Our strategy proceeds in three stages:
Ground- Truth Residual Logging. We run full inference on small calibration set and record ground-truth residuals ydt (2,, for all steps.
Threshold-Bes Policy Search. For range of candidate thresholds X, we evaluate whether the cached residual pealt is sufficiently close the freshly computed one using the relative & error:
|| (vra 1)11
(12)
|| 1)12
If (Fre)( t) < A. the cache is reused; otherwise, the model recomputes and updates the cache. Sweeping across values of we derive a reusable cached timestep set {so, 88).
Inference-Time Cache Application. During the inference stage, the model initializes with -T and caches the residual = UT IT- Then, for each step t, if t e S, it reuses f; otherwise, computes v; from scratch and refreshes the cache F.
The choice of À involves trade-off:: lower values preserve finer details through frequent cache updates, whereas higher values prioritize generation speed at the potential cost of visual fidelity. Optimal threshold selection should balance these competing objectives.
Trajectory-Awary Timestep Adjustment A naïve reuse cached features assumes strict adher- ence to the original ODE trajectory. However, as visualized in Figure 2b this leads to noticeable trajectory drift, especially when fixed timestep intervals are maintained. To counteract this devia- tion. introduce trajectory- timestep adjustment mechanism, where correction coefficient Φe e [0, 1] dynamically modifies the timestep size.
Initially, At, = 1/TT for uniform sampling.
For each step i, we apply:
Ator Ale Øs;
if $ S, ifi
At,
(13)
with di = clip (1- ||0
0, 1).
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Published as a conference paper at ICLR 2026
After each update, the residual timestep budget is adjusted by following:
A
A
(14)
i/T
This adaptive policy helps align the actual trajectory with the intended sampling path, even under aggressive reuse. The simplified update workflow depicted in Figure
Explicit Error Rectification via Residual I Linearization To further mitigate error accumulation, we introduce an explicit error modeling component. Since the additive error E; o o2 is difficult to predict directly due to prompt-specific structure, approximate it using lightweight linearized model:
o(K 8 B,), where is sigmoid, and N₁ No Nv. No. where No, No. No. denote dimension of time, height and width for latents.
(15)
The mean squared error loss is defined
n
L(K,) Σ
Eig - (Kiutt Biz)]²
(16)
j=1
Using a first-order Taylor approximation of the sigmoid:
(K₃0; + B,) 4 (K₃ + B,) + 2
(17)
Substituting into the loss function and computing the partial derivatives w.r.t. B; and K2, we obtain:
ØL.
(Kigūı + Biÿ) - 1 -
0B,
=
2n
Eij 4
(18)
j=1
0L
1
0K,
= 2n /=1
= (K + +B,)) 7 = 0.
(19)
Let:
E, 1 Σ
11 Eije
0, 1 -2
~n Uij,
(20)
Sp,t. クz=1 (Di 0z)3
プ5 (ōe (:) E:). Suze,
An approximate closed-form solution can be derived as follows (see Appendix for detailed deriva- tion):
K; R2 4.
R 4(E, - 4KGE,
(21)
These values can be precomputed from small extracted dataset and reused during inference to provide error-correctd cached outputs.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Base Models and Compared Methods To substantiate the generality and efficacy of our pro- posed ERTACache, we integrate into state-of-the-art diffusion backbones spanning both video and image generation. Concretely, we evaluate Open-Sora 1.2 Zheng al. (2024). Videod Yang et al. (024), Wan2.1 Wan et al. (2025), and Flux-dev 1.0 Labs (2024). We benchmark these mod- els against leading training- free acceleration techniques namely, PAB ZZho et al.) A-DiT Chen et al. (2024), FasterCache Lv et al.) (2024), ProfilingDiT Ma et al. (2025), and TeaCache Liu et al.
(2025b to isolate and quantify the incremental gains conferred by our approach.

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Published as a conference paper at ICLR 2026
Efficiency
Visual Quality
Method
Speedupt
Latency (s))
VBecch f
LPIPS
SSIM
PSNR
Open-Sora 1.2 (51 frames, 480P)
Open-Sora 1.2 (T 30)
1x
44.56
79.22%
A-DiT Chen et al.
2024
1.03
78.21 %
0.5692
0,4811
11,91
T-GATE
2025c
1.19x
77.61 %
0.3495
0.6760
15.50
PAB-slow Zhao
1.33x
33.40
77.64%
0.1471
0.8405
24.50
PAB-fast Zhao et al.
1,40x
31.85
76.95%
0.1743
0.8220
23.58
TeaCache-slow Liu et al.
2025b
1.55x
28.78
79.28%
0.1316
0.8415
23.62
TeaCache-fast Liu et 202551
2.25x
19.84
78.48%
0.2511
0.7477
19.10
ERTACache-slow (A=0.1)
1.55x
28.75
79.36 %
0.1006
0.8706
25.45
ERTACache-fast (A=0.18)
2.47x
18.04
78.64 %
0.1659
0.8170
22.34
CogVideo' (48 frames, 480P)
VodeiX (T 50)
1x
78.48
80.18%
A-DiT (N.
= 2)
1.08
72.72
79.61% %
0.3319
0.6612
D-DiT (N₂ 8, N = 2)
1.15x
68.19
79.31 %
0,3822
0.6277
16.69
DiD (N. N 2)
1.26x
62.50
79.09%
0,4053
0,6126
16.15
PAB Zhao et al.
1.35x
57.98
79.76%
0.0860
0.8978
28.04
FasterCache al.
2024
1.62x
48.44
79.83%
0.0766
0.9066
28.93
TeaCache Liu et al. 2025b)
2.92x
26.88
79.00%
0.2057
0.7614
20.97
ERTACache-slow (A=1.08)
1.62x
48.44
79.30 %
0.0368
0.9394
32.77
ERTACache-fast (A=0.3)
2.93x
26.78
78.79%
0.1012
0.8702
26.44
Wan2.1-1.3B (81 frames, 480P)
Wan2.1-1.3B (7' 50)
1x
199
81.30%
TcaCache Liu ct al. (2025b)
2.00x
99.5
76.04%
0.2913
0.5685
16.17
ProfilingDiT Ma al. 2025
2.01 x
99
76.15%
0.1256
0.7899
22.02
ERTACache (X=0.08)
2.17x
91.7
80.73% %
0.1095
0.8200
23.77
Table 1: Quantitative comparison video generation models. f: higher is better; J: lower is better.
Efficiency
Visual Quality
Method
Speedupt
Latency (s)
CLIP
LPIPS
SSIM f
PSNR
Flux-dev 1.0 (T' 30)
26.14
TeaCache Liu et al. (2025b)
1.84x
14.21
0.9065
0.4427
0.7445 0.8962
16.4771
ERTACache (A 0.6)
1.86x
14.01
0.9534
0.3029
20.5146
Table 2: Quantitative comparison in Flux-dev 1.0. f: higher is better; J: lower is better.
Evaluation Metrics We assess both video and image generation accelerators along two orthogonal axes: computational efficiency and perceptual fidelity.
Efficiency. Inference Iatancy-measured end-to-end from prompt ingestion to final frame reported speedup compared the base models.
Visual Quality. We adopt multi-faceted protocol that combines human-centric
benchmark with established similarity metrics, including: VBench Huang et al. (2024b). LPIPS Zhang al. (2018), PSNR, SSIM and
al. (2021).
Together, these metrics yield a holistic view of how aggressively an acceleration method trades computational speed for generative fidelity.
Implementation Details For the primary quantitative analysis on the VBench default prompts, we synthesize five videos per prompt with distinct random seeds on NVIDIA A800 40 GB GPUs. To guarantee reproducibility, prompt extension is disabled and all other hyper-parameters are set to the official defaults released by each baseline. This protocol mirrors the evaluation pipelinesof recent studies Liu et al. (2025b); Zhao et al., ensuring rigorously fair comparison of generation quality. To accelerate ablation studies, we further adopt a lightweight configuration in which 1-n2.1-1.3B generates single video per prompt with seed O.

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Published as a conference paper at ICLR 2026
Original
TecCuche
ERTA (0
CogvideuX-2B
1,00X
2,9%, PSAR 20.7
2,0%, PSNR 26.4
Wan2.1-1.38
OpenSora
1,00x
2.25% PSNR 19.1
2.47, PSNK 12.54
1,86,, PSNR 2151
Figure 3: Comparison of visual quality and computational efficiency against competing approaches, illustrated by the first and last frames generated video sequences.
4.2 MAIN RESULTS
Quantitative Comparison and Table present quantitative evaluation on VBench, com- paring efficiency and visual quality across baseline models. We assess two variants of ERTACachh: a slow version (lower A) for higher quality, and fast version (higher X) for greater speedup. Both variants consistently achieve strong acceleration and superior visual fidelity across diverse video and image synthesis models. On Open-Sora 1.2 Zheng et al. (2024). ERTACache-slow and ERTACache- fast yield speedups of 1.55x and 2.47 x, respectively, while outperforming A-DiT, PAB, and Tea- Cache on all quality metrics. For Cog CogVideoX, ERTACache achieves significant improvements in LPIPS (0.1012)), SSIM (0.8702t), and PSNR (26.44*) compared TeaCache (0.2057, 0.7614, 20.97), with comparable 2.93x speedup. With 1an2.1, 1, we attain the best visual quality and competitive 2.17x speedup. ERTACache also demonstrates strong performance on the Flux-dev 1.0 image synthesis model, achieving the highest visual quality with 1,86x speedup, matching TeaCache.
Visualization The strength of ERTACache lies in its ability maintain high perceptual fi- delity while ensuring strong temporal coherence. In Figure ERTACache preserves fine- grained vi- sual details and frame-to-frame to-frame consistency, outperforming TeaCache Liu et al. (2025b) and match- ing the non-cache reference. In video generation tasks using Cog VideoX, Wan2.1-1.31 and Opera- Sora, ERTACache achieves noticeably better temporal consistency, particularly between the first and last frames. When applied to the Flux-dev 1.0 image model, enhances visual richness and detail. These results highlight ERTACache as uniquely effective solution that balances visual quality and computational efficiency for consistent video generation.
4.3 ABLATION STUDY
We perform ablation studies on Wan2.1 and Flux-dev 1.0 to evaluate the individual impact of the offline-searched policy, timestep adjustment, and rectification, shown in Table These results validate the effectiveness and efficiency of each component in our framework. We assess the sensitivity of ERTACache to varying data volumes, validating its robustness with minimal computational overhead of less than 0.5%. Further details regarding this analysis are provided in the Appendix.
Advantages of Offline Policy Compared to Uniform Cache, the proposed Offfine Policy effec- tively mitigates information loss, leading to consistent improvements across all evaluated metrics on both video and image synthesis models. It is noteworthy that the Offline Policy yields statistically
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Wan2.1-1.3B
VBench*
LPIPS!
SSIM
PSNR
Uniform Cache
79.35%
0,5041
0.4058
13.7640
Offline Policy
80.59%
0.1477
0.7738
22.0920
+Time Adjustment
80.89%
0.1267
0.7988
22.9413
+Error Rectification
80.73%
0.1095
0.8200
23.7680
Flux-dev 1.0
CLIPT
LPIPS
SSIM
PSNR
Uniform Cache
0.9066
0.4424
0.7105
15.9428
Offline Policy
0,9433
0.3469
0.8693
19.4069
+Time Adjustment
0.9510
0.3268
0.8921
20.3586
+Error Rectification 0.9534
0.3029
0.8962
20.5146
Table 3: Ablation study Wan2.1-1.38 and Flux-dev 1.0.
Original
Uniform Cache
+Offiie Pulicy
Timee Adjustment
Error Rectification
Wan2.1-L.3B
person doing laundry"
Flu-dev
Lafthanzsa atrline flighe flying the sky
Figure 4: Visualization effects of each strategy in ERTACache.
significant improvement of 1.24% in the composite VBench metric on the Wan2.1-1.38 indicating substantial enhancements in temporal consistency, motion authenticity, and visual aesthetics.
Advantages of Timestep Adjustment The Time Adjustment mechanism enhances structural co- herence, yielding significant improvements in evaluation metrics. For instance, on the Flux-dev 1.0, integrating this adjustment with the Offline Policy improves PSNR and SSIM by 0.9517 and 0.0228, respectively, compared to using the Offline Policy alone.
Advantages of Error Rectification Error Rectification further improves perceptual fidelity by effectively correcting generation artifacts. As demonstrated in Figure this module significantly enhances output quality through systematic error mitigation. Cumulatively. the full pipeline demon- strated notable gains in CLIP, LPIPS and PSNR versus baseline. This hierarchical progression demonstrates complementary component effects: offline policy enables foundational recovery, time adjustment optimizes temporal consistency, and error rectification maximizes preservation of detail, collectively resolving key efficiency- quality tradeoffs.
CONCLUSION
In this work, we present ERTACache, principled and efficient caching framework for acceler- ating diffusion model inference. By decomposing cache-induced degradation into feature shift and step amplification errors, we develop a dual-path correction strategy that combines offline-calibrated reuse scheduling, trajectory-aware timestep adjustment, and closed-form residual rectification. Un- like prior heuristicy-bawe methods, ERTACache provides a theoretically grounded yet lightweight solution that significantly reduces redundant computations while maintaining high- figh-fideitty outputs. Empirical results across multiple benchmarks validate its effectiveness and generality, highlighting its potential practical solution for efficient generative sampling.

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6 ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics. this study, no human subjects or animal ex- perimentation involved. All datasets used, including VBench, were sourced in compliance with relevant usage guidelines, ensuring no violation of privacy. We have taken care to avoid any biases or discriminatory outcomes our research process. No personally identifiable information was used, and no experiments were conducted that could raise privacy or security concerns. We are committed maintaining transparency and integrity throughout the research process.
REPRODUCIBILITY STATEMENT
We have made every effort to ensure that the results presented this paper are reproducible. All code and datasets have been made publicly available in an anonymous repository to facilitate repli- cation and verification. The experimental setup, including training steps, model configurations, and hardware details, is described in detail in the paper. We have also provided full description of the evaluation metrics, hyperparameter settings, and any preprocessing steps applied to the data, as- sist others in reproducing our experiments. We believe these measures will enable other researchers to reproduce our work and further advance the field.
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A
APPENDIX
A.1 ALGORITHM
In this section, we elaborate on the inference algorithm of ERTACache, encompassing the implemen- tation of cache inference using Timestep Adjustment and Error Rectification methods (Algorithm as well as the acquisition of static cache list through Offline Policy Calibration (Algorithm
Algorithm ERTACache Inference with Timestep Adjustment and Error Rectification
1: Input: Transformer model M, total step T, cached timesteps set S, Fixed parameter K, B
2: Output: Final output Zo
I/ Trajectory-Aware Timestep Adjustment
3: Initialize Ale 1/T
4: for = T 1 ..... do
5: if E S then
6: At, At.
7:
Φi clip
1 11
0,
10 1
1- Σ Ai
8:
At i/7
9: else
10:
At, Ate
11: end if
12:
end for
If ERTACache Inference
13: Sample 2T-1 (0, i T [0,
14: for 1, do
15:
i 7 -1 then
16:
i E S then
x; 11+1 1 + Att1 (1141 (Ki+11144 B(41))
18:
else
2; = 1 = + At+t 0:41
20:
end if end if
21:
22:
if E S then
23:
6 = +
24:
else
25:
1i - compute output of the M
26:
f 2/4 T2
27:
end if
28:
end for
PSNR
SSIM
CLIP Score
10.9495
19.4239
19.5304
0.8675
0.9425
0.9450
Num
Num
Num
Figure 5: Illustration of different metrics using different number of prompts with timestep adjust- ment.
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Algorithm 2 Offline Policy Calibration via Residual Error Profiling
1: Input: Transformer model M, batched calibrate prompts set Pe, total step T, threshold A
2: Output: Cached timesteps set S
3: Sample 2T-1 _N(0, I). i +- - t [0,
If Ground-Tiie Truth Residual Logging
4: for = L.., do
5: D2 < compute output of the M for Pa
6: (x,, 0/2 3;
7: end for
// Threshold-Bast
Policy Search
8: for i = T 1.... 0 do
9:
0i f- compute output of the M
10:
(2,, t) =
if T or i = 0 then
12:
poali 14
13:
else
poals
14:
ret (zi,, 1)
1 Oll
15:
if lrel < X then
put 2 cached timesteps set S
17:
tú I2 + pooti
else
19:
poati 02 end if
20:
21:
end if
22:
end for
Model
Method
Memory Usage (MiB)
Latency (s)
Baseline
26988
26.14 14
TeaCache Liu al. (005b)
27056
14.21
FLUX-dev 1.0
ERTACache Offinne Policy (+Time Adjustment)
27020
13.41
ERTACache Offline Policy (+Error Rectification)
27022
14.01
Baseline
21558
44.56
TeaCache Liu et al.
(025b)
21558
19.84
OpenSora 1.2
ERTACache Offine Policy (+Time Adjustment)
21346
17.79
ERTACache Offline Policy (+Error Rectification)
21346
18.04
Table 4: Memory Usage and Latency on a single NVIDIA A800 40GB GPU.
A.2 ANALYSIS OF ERROR RECTIFICATION IMPACT
Sensitivity Analysis In the Error Rectification process, we found that averaging the cache emrors collected at each step under different prompts can improve efficiency and optimize model performance compared with determining the K value through model training. We explore the impact of the number of prompts on the final results evaluate the generality and accuracy of the averaging method. As shown in the Figure the obtained different K values all exhibit stable trends in various metrics (PSNR, SSIM, CL.IP Score) on the same medium-sized test dataset (data volume 1000, based on MS COCO 2014 evaluation dataset) under different numbers of prompts (num = 20, 30, 1000). The experimental results demonstrate that the averaging method has strong stability and generality. and it can generalize to correction tasks in other scenarios with very small batch of real errors, thus possessing the advantages of low cost and high efficiency.
Extra Computational Cost This study compares memory usage among three methods: No cache added, TeaCache Liu et al. (025b), and our proposed ERTACache. The experimental results demon- strate that ERTACache exhibits a slight advantage in terms memory usage (see Table The core reason lies in the fact that ERTACache adopts offline method to pre-determine fixed cache list,
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toilet, frozen time
Dhaee
Chomal)
Dhnndl
Cogvideo.X-2B
laptop, frozen in time
OpenSora 1.2
In still frame, a stop sign
1-12,1
cutoo
Figure 6: Illustration of cache error using different model, displaying 3 different channels of the first frame
thus eliminating the need for additional computation of modulated inputs and prediction of whether caching is required at the beginning of each step, thereby avoiding extra runtime computational overhead. Additionally, evaluating the Error Rectification parameter K reveals negligible impacts on both memory usage (FLUX-dev 1.0: +0.007%; OpenSora 1.2: +0%) and latency (FLUX-dey 1.0: +0.45%; OpenSora 1.2: +0.14%) across text-to-image and tot-to-video scenarios.
A.3 ERROR RECTIFICATION DERIVATION
The general formula for the accumulates error during the diffusion trajectory is:
m-1
8,
At;-
(22)
Am
where e; is unknown cache eтrоr at i-th step. In past experiments, it has been found that each step of e; contains structural information based on different prompts (see Figure(6). which has become difficulty in the general error formula. Therefore, output to make a generalized prediction for e,. For different prompts, is assumed that there is a fixed high- dimensional tensor K,, B, and an activation function such that:
attempt is made to combine the model cached
0g(xI, p0(x,, t) o (Kę (0( + Bt),
(23)
a (xx) 1-
(24)
where (0 t) is the model cached output at i-th step. and po t) is the original model output at i- step, (xx) selected as the Sigmoid function. This method is equivalent to defining a single-layer convolutional network mapping structure, which can better accommodate the structural information brought by different prompts and make e, universal.
Let the approximate model A; aK,ū + B.). We utilize the mean squared error as the loss function to minimize the error between predicted outputs A, and ground-truth values E, where N₂ N. x N₁, N.:
Nr L(Ki) ΣΣMM
1 n n
(Ei A
-
Σ Jey (Kizojj + B.)) (25)
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Derivative with respect to K; and B;
OL 2
Eij (Ki + B1y) (Kiyūi (K Biy] (-1) 0. (26)
0B.
n
j=1
OL. 2 n
Ciy (K3 + Bıy)l (Ki + B,y)[1 - oKKyou + Bsy)l (-0sy) = 0. (27)
0K;
1=1
Since the Sigmoid function is nonlinear equation and the system of equations contains the coupling terms of K and B the same time, the above system of equations cannot directly find analytical solution. The Taylor expansion method is used for approximate linear processing:
(xx)
+
+
3
+
+ 0
2
4 48
Then we have:
1
A ~ (K + B,) +
(28)
2
Substituting into the loss function, we derive:
1
172
L
Etj
(Kzg + Bas))
(29)
n
2
Derivative with respect to K; and Bi
aL
1
n
1
Eij
(Kijūis Biz)
0.
(30)
0B.
2n
2
j=1
0L
n
1 (Ki 02g + Buj)
1
aK;
2n
Eij
2
0.
(31)
LINEAR SYSTEM OF EQUATIONS AND SOLUTION
After arrangement, linear system of equations obtained:
Σー= (Et K B,B - 21) =0= 0,
(32)
ΣS dig (Eis - K. - 1 })) =
It is further simplified to:
n (3 +  K プド 0ij = =ド Egj
(33)
B. プラ Uig + K.
j=1 UH
+ ト ūij
Let:
E; 1 T
- Sijs
02 Σ j=1 Kig.
(34)
Sace 2ー=1( (ūg
|=
12 -no
So,s, E)=(01 (ūt
0y (Eij
E, Σソ= UijCij no:e,
The approximate closed-fom solution can be obtained:
Suie S
K2 Rs 4
B, Rd
Es -6--
4K,0
(35)
2
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A.4 LLM USAGE
We only used the large language model for text polishing, solely to refine linguistic expression (e.g., sentence structure, fluency, terminological consistency) while strictly preserving the original scientific content and conclusions. No other research steps involved any large language models.
17


Reviews:
[{"review_id": "F5eewbxHRW", "reviewer": "Reviewer_pTb2", "summary": "This paper presents ERTACache, an error-rectified caching framework designed to improve the efficiency of diffusion-based inference. The method introduces a modular strategy that combines: (1) offline cache calibration to identify redundant denoising steps; (2) trajectory-aware time-step adjustment using a residual-driven coefficient $\\phi_i$ to suppress drift accumulation; and (3) explicit error rectification that analytically re-injects residuals to maintain stability. Across several diffusion benchmarks, ERTACache achieves roughly $2×$ acceleration with negligible loss in visual quality, requiring no retraining or architectural modification. The approach provides a practical trade-off between efficiency and fidelity, complementing prior acceleration techniques such as dynamic skipping, pruning, and distillation.", "questions": "1. How well does the linear residual approximation hold under highly non-linear diffusion dynamics or alternative noise schedules?\n\n2. Could ERTACache be combined with adaptive ODE solvers, and how might local error control interact with the correction term?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "ERTACache demonstrates strong advantages in **error modeling depth**, **cross-task adaptability**, and **quality–efficiency balance**, providing a theoretically grounded solution to the long-standing problem of *error accumulation* overlooked by prior caching methods.\n\n1. Existing caching methods (e.g., TeaCache, PAB) rely on *heuristic thresholds* for redundancy detection and lack theoretical understanding of how caching degrades output quality, often resulting in the phenomenon “higher speed → worse quality.”  \n   **ERTACache’s core innovation** lies in its formal *dual-source decomposition* of caching error (Eq. 11):  \n   - **Feature Shift Error (εᵢ = ṽᵢ − vᵢ):** deviation between cached and true features, originating from inaccurate reuse.  \n   - **Step Amplification Error (δᵢ₋ₘ = Σₖ₌₀^{m−1} Δtᵢ₋ₖ ⋅ εᵢ₋ₖ):** cumulative error propagation under fixed step size via ODE integration, amplifying earlier deviations.  \n   This theoretical decomposition reveals, for the first time, the *root causes of quality loss in caching*, paving the way for principled correction strategies.  \n   **Closed-loop error correction mechanism:** ERTACache jointly addresses both error types through (1) offline step selection to minimize low-error reuse, (2) dynamic timestep adjustment to reduce amplification, and (3) residual linearization to directly correct feature shifts — forming a full *“error identification → prevention → correction”* loop rather than a one-shot caching heuristic.\n\n2. ERTACache achieves *high acceleration with near-lossless quality* in both video and image generation without retraining:  \n   - **Video Generation:**  \n     - *Open-Sora 1.2 (51f, 480P):* ERTACache-fast attains 2.47× speed-up (18.04 s vs 44.56 s), VBench 78.64% (−0.58%), LPIPS 0.1659 (vs TeaCache-fast 0.2511), PSNR 22.34.  \n     - *CogVideoX (48f, 480P):* 2.93× speed-up with LPIPS 0.1012, SSIM 0.8702, PSNR 26.44 (TeaCache 20.97).  \n     - *Wan2.1-1.3B (81f, 480P):* 2.17× speed-up, VBench 80.73% (vs 81.30%), LPIPS 0.1095, SSIM 0.8200 — far better than TeaCache (LPIPS 0.2913, SSIM 0.5685).  \n   - **Image Generation:**  \n     On *Flux-dev 1.0*, 1.86× speed-up yields CLIP 0.9534 (vs TeaCache 0.9065), PSNR 20.51 (+4.34), preserving finer visual details (Table 2, Fig. 3).\n\n3. ERTACache balances theoretical rigor with practical deployment:  \n   - **Offline Policy Calibration:** A small calibration set (no extra data) precomputes *true residuals* using relative $L_1$ error $\\ell_{1rel}$, forming a reusable step set $S$ for inference without runtime computation. Even with only 20 prompts, performance matches a 1000-sample calibration (Appendix A.2).  \n   - **Trajectory-Aware Timestep Adjustment:** Defines correction factor  \n     $\\phi_i = \\text{clip}(1 - \\frac{\\|v_i - v_{i+1}\\|_1}{\\|ṽ_i - v_i\\|_1}, 0, 1)$  \n     to dynamically adapt step size $Δt_i = Δt_c ⋅ φ_i$, aligning sampling trajectories with true ODE paths. On Flux-dev 1.0, this boosts PSNR by +0.95 (Table 3).  \n   - **Low sensitivity to hyperparameters:** With $\\lambda$ ranging 0.08–0.3, VBench varies only 0.3–1.2% (Table 1, 3), requiring no per-model retuning.\n\n4. **Strong Theoretical Foundation — Precise Error Correction via Residual Linearization**  \n   Instead of heuristic compensation, ERTACache employs a *closed-form linearized residual model* for direct error correction:  \n   - **Lightweight model:** Approximates feature error as $ε_i ≈ σ(K_i ṽ_i + B_i)$ , deriving closed-form solutions for $K_i, B_i$ via Taylor expansion (Appendix A.3), adding <0.5% latency.  \n   - **Effective correction:** On Wan2.1-1.3B, correction reduces LPIPS from 0.1267 → 0.1095 and increases PSNR 22.94 → 23.77 (Table 3), eliminating frame jitter and detail blur common in TeaCache (Fig. 3–4).", "weaknesses": "1. **Reduced Correction Accuracy in Highly Dynamic Scenes**  \n   The “universal error model” assumption holds for static scenes (e.g., still objects) but struggles under dynamic motion:  \n   - The closed-form coefficients $(K_i, B_i)$are calibrated offline and assume stationary error distribution, which breaks under rapidly changing dynamics. Fig. 6 shows distinctly different error-channel distributions between dynamic and static prompts, yet no adaptive adjustment is made.  \n   - Experiments use mostly *low- to medium-motion* prompts (“washing clothes,” “airplane flight”) without validating performance on highly dynamic scenes.\n\n2. **Offline Calibration Generalization Depends on Dataset Quality**  \n   Though small calibration sets suffice, the method lacks analysis of *domain shift* effects:  \n   - If calibration data are “natural landscapes” but deployment involves “abstract art,” the precomputed reusable-step set S may mispredict high-error steps.  \n   - No guidance on calibration-set composition or prompt diversity is provided, increasing deployment trial cost.", "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": "vuKBU2x8Z1", "reviewer": "Reviewer_dLSR", "summary": "This paper proposes ERTACache, a training-free caching framework for diffusion models. It decomposes cache-induced degradation into feature shift and step amplification errors, and introduces three modules to mitigate them: (1) Offline residual profiling for selecting reusable steps (λ-based policy), (2) Trajectory-aware timestep adjustment using a scaling factor ϕt, and (3) Linearized residual correction with closed-form parameters Ki and Bi. Experiments on Open-Sora, CogVideoX, Wan2.1, and Flux-dev show about 2× speedup while maintaining comparable visual quality.", "questions": "See Weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1.The paper provides a clear and interpretable error decomposition, distinguishing the two main error sources of caching in diffusion models. This theoretical framing is well-motivated and guides the method design.\n\n2.Comprehensive experiments across multiple video and image backbones demonstrate the generality of the approach. The comparisons with strong baselines (TeaCache, FasterCache, PAB, etc.) show consistent efficiency–quality improvements.\n\n3.The ablation study clearly isolates contributions from each component, verifying that the offline policy, timestep adjustment, and error rectification complement one another.", "weaknesses": "The method relies on an offline calibration set and a threshold λ to select reusable steps, but the paper does not analyze how the calibration set size, diversity, or λ values affect the performance across different domains. A sensitivity study on λ and calibration configuration would strengthen the empirical validation.", "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": "qMJO4Fgpej", "reviewer": "Reviewer_uKah", "summary": "This paper addresses the computational inefficiency of diffusion models and the quality degradation issue of naive feature caching, proposing ERTACache, a caching framework that decomposes cache-induced errors into feature shift and step amplification errors, and mitigates them via offline residual profiling, trajectory-aware timestep adjustment, and closed-form residual linearization.\nExtensive experiments on Flux-dev 1.0 and Open-Sora 1.2, CogVideoX, Wan2.1 diffusion models show ERTACache achieves up to 2.47× inference speedup while preserving or improving visual quality.", "questions": "1. Regarding novelty: Since the core components of ERTACache (offline calibration, dynamic timestep adjustment, closed-form error correction) are derived from adapting existing techniques (e.g., precomputed steps in DPM-Solver, error compensation in numerical methods), what unique mathematical properties of diffusion model cache errors does this framework reveal to distinguish it from \"technical migration\" rather than \"fundamental innovation\"?\n\n2. On method implementation: In the trajectory-aware timestep adjustment, the correction coefficient uses the denominator ||v_i - v_{i+1}||₁. How is numerical instability avoided when this denominator approaches zero (e.g., to prevent division by a near-zero value)?\n\n3. For method robustness: The offline policy calibration relies on a fixed threshold λ (e.g., 0.08 for Wan2.1, 0.6 for Flux-dev). Is there an adaptive mechanism to determine λ automatically across models, and how stable is ERTACache’s performance when λ fluctuates by 20%?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Formalizes and addresses two core cache-induced errors in diffusion models (feature shift error and step amplification error) via a targeted dual-correction strategy, which differentiates from prior caching methods that lack explicit error decomposition and mitigation.\n2. Integrates three training-free components (offline residual profiling for reusable steps, trajectory-aware timestep adjustment, closed-form residual linearization) to enable both high efficiency and fidelity, avoiding the trade-off of either high memory cost (e.g., AdaCache) or quality degradation in existing works.\n3. Achieves superior performance balance on video diffusion models (e.g., 2.17× speedup with 80.73% VBench on Wan2.1 vs. TeaCache’s 2× speedup and 76.04% VBench) while maintaining comparable memory to baselines.", "weaknesses": "1. Insufficient details on key implementation aspects: e.g., no clarity on the calibration dataset’s specifics (size, domain) for offline policy tuning, or the exact logic for selecting the optimal threshold λ across models.\n\n2. The paper did not verify performance under extreme conditions: no tests on long-duration videos (e.g., >100 frames), ultra-high resolutions (e.g., 1080P/4K).\n\n3. Lacks human subjective evaluation to complement objective metrics (e.g., VBench, PSNR), which may lead to inconsistencies between reported numerical results and actual perceptual quality.", "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": "1Cw6GxZZz1", "reviewer": "Reviewer_KJQB", "summary": "The authors theoretically evaluate the error accumulation from cache-based acceleration for diffusion model in video generation, and summarize the error as a summed product of step amplification error and feature shift error. Targeting the two error components, the authors propose ERTACache that performs (1) offline policy calibration that runs full inference on a calibration set, finding optimal steps to cache based on threshold; (2) adaptive timestep adjustment to dynamically control the diffusion trajectory, and (3) explicit error rectification that uses a linear model to approximate the error for a small extracted dataset, then use the same model to correct in inference.", "questions": "1. How specifically are the three methods (offline policy calibration, trajectory-aware timestep adjustment, and explicit error rectification) related to the two error sources (feature shift and step amplification)? I understand that the methods are to reduce error, but how are the two error sources specifically targeted? \n2. In Table 3, is Offline Policy built above Uniform Cache? Looks like the Offline Policy contributes the most to generation quality. \n3. Probing using a small dataset looks important in this work. Does it potentially lead to overfitting? \n4. Could you also provide speedup evaluations in ablation study?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The theoretical analysis clearly pinpoints and characterizes the error sources in cache-based acceleration. \n2. The methods proposed are general.", "weaknesses": "1. After pointing out the feature shift and step amplification errors, they are no longer mentioned in the methods. \n2. The methods are relatively simple with limited effectiveness. \n3. Minor writing clarity issues. e.g., \"Figure 2\" is missing in the figure caption, and TACache in the figure is not defined. There are other typos, too.", "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)