Towards One-step Causal Video Generation via Adversarial Self-Distillation
Authors: Yongqi Yang
Huayang Huang
Xu Peng
Xiaobin Hu
Donghao Luo
Jiangning Zhang
Chengjie Wang
Yu Wu
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Distillation
Causal Text to Video Generation
Primary area: generative models
TL;DR: Our novel distillation framework with self-distillation and specific inference strategy accelerates text to video generation while maintaining high quality.

Abstract: Recent hybrid video generation models combine autoregressive temporal dynamics with diffusion-based spatial denoising, but their sequential, iterative nature leads to error accumulation and long inference times. In this work, we propose a distillation-based framework for efficient causal video generation that enables high-quality synthesis with extreme limited denoising steps. Our approach builds upon Distribution Matching Distillation (DMD) framework and proposes a novel form of Adversarial Self-Distillation (ASD) strategy, which aligns the outputs of the student model's $n$-step denoising process with its $(n+1)$-step version in the distribution level. This design provides smoother supervision by bridging small intra-student gaps and more informative guidance by combining teacher knowledge with locally consistent student behavior, substantially improving training stability and generation quality in extremely few-step scenarios. In addition, we present a First-Frame Enhancement (FFE) strategy, which allocates more denoising steps to the initial frames to mitigate error propagation while applying larger skipping steps to later frames. Extensive experiments on VBench demonstrate that our method surpasses state-of-the-art approaches in both one-step and two-step video generation. Notably, our framework produces a single distilled model that flexibly supports multiple inference-step settings, eliminating the need for repeated re-distillation and enabling efficient, high-quality video synthesis.

Published as a conference paper at ICLR 2026
TOWARDS ONE-STEP CAUSAL VIDEO GENERATION VIA ADVERSARIAL SELF-DISTILLATIO
Yongqi Yang': Huayang Huang : Xu Peng Xiaobin Hu², Donghao Luo
Jiangning Zhang², Chengjie Wang", Yu Wu
1 School of Computer Science, Wuhan University Tencent YouTu Lab
"Scoool of Artificial Intelligence, Wuhan University
lyongqiyann yang, hyhuang, wuyucs wwh.:euu.uucceuueeeccoeee cn
edu.
Project page: https: gig.................... com cwm/BigAandmmma Llg /tD.///S..DD. git
ABSTRACT
Recent hybrid video generation models combine autoregressive temporal dynamics with diffusion-based spatial denoising, but their sequential, iterative nature leads eгrоr accumulation and long inference times. In this work, we propose a distillation-based framework for efficient causal video generation that enables high-quality synthesis with extreme limited denoising steps. Our approach builds upon Distribution Matching Distillation (DMD) framework and proposes novel form of Adversarial Dist-Diiillation (ASD) strategy, which aligns the outputs the student model''s n-step denoising process with its (n+1) (n-se) version in the distribution level. This design provides smoother supervision by bridging small intra-student gaps and more informative guidance by combining teacher knowledge with locally consistent student behavior, substantially improving training stability and generation quality in extremely few-step scenarios. In addition, we present a First-Frame Enhancement (FFE) strategy, which allocates more denoising steps the initial frames mitigate error propagation while applying larger skipping steps to later frames. Extensive experiments on VBench demonstrate that our method surpasses state-of-the-ar approaches in both one-step and two-step video generation. Notably, our framework produces single distilled model that flexibly supports multiple inference-step settings, eliminating the need for repeated re- distillation and enabling efficient, high-quality video synthesis.
INTRODUCTION
Diffusion models (Ho et al. 2020; Song al.) 2021a| ll Nichol & Dhariwal 2021 have achieved
remarkable progress in high-quality image (Dhariwal & Nichol| 2021 Nichol et al. 2022 Rombach ct al, 2022) and video generation (Ho et al. 2022] Brooks et al.] 2024; Blattmann et al. (023).
However, their application to long-form video CChnn et al. [023] Zhang et al. 2023) and interactive settings (Che et al.) 2025) remains limited by the high computational cost of iterative denoising and the reliance bidirectional attention, which requires synthesizing entire sequences jointly. Autoregressive methods (Liang et al., 2022: Ge et al. 2022 Wang et al. 2024), in contrast, allow causal frame-by-frane generation, but often suffer from error accumu accumulation lation that compromises
realism. Recent works (Chen et al.] 2024 Jin et al. [2025] Li et al. 2025) have attempted to combine autoregressive temporal modeling with diffusion--aseed spatial refinement, yet these hybrid approaches inherit the efficiency bottleneck of multi-step denoising.
To alleviate this limitation, distillation has emerged as a promising direction for accelerating diffusion models (Sauer et al.] 2024b) Yin et al., [2024b] Lu et al. 2025). By reducing a multi-step model to a few-step counterpart, distil lation greatly improves efficiency while preserving generation quality. Nonetheless, existing distillation objective primarily focuses on aligning the prediction distributions of the few-step student with those of the multi-step teacher. When the student performs only 1- or 2-step generation, the discrepancy from the multi-step teacher becomes excessively large, making
"quual contributions. Work done during an internship at Tencent YouTu Lab.
Corresponding authors.

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Published as a conference paper at ICLR 2026
direct alignment unstable and causing severe quality degradation (Cheng et al. 2025 Yin et al. 2025 Huang et al. 2025). In other words, the fewer denoising steps, the larger the semantic and statistical gap to be bridged, which explains why extremely few-step distillation is particularly challenging.
In this paper, we introduce a novel method to address the challenge of high-quality video gen- eration with minimal denoising steps (e.g.
Distribation Matching Distillation
Adversarial Self-Distillation
Panta
Paus
and steps). We extend the distribution match- ing distillation (DMD) framework (Yin et al.) 2024b a), and propose novel Adversarial Self- Distillation (ASD) strategy. Unlike prior dis-
Pa-gm
LEMD
Pa-gen
DAMD
Pa-gen
LASD
tillation methods (Song al., 2023, Song & Dhariwal 2024, Lin et al., 2024) that rely solely on supervision from the multi-step teacher, ASD provides the student with additional guidance from its own intermediate variants with slightly different denoising steps. Specifically, dis- criminator is employed to adversarially match the n-step and -step denoising distribution. As shown Fig. | this step-wise self-alignment has two advantages: (1) it produces smoother su- pervision by bridging smaller step-to-step gaps instead of the large teacher-student gap, and (2) yields more informative signals, since the student learns from both global teacher knowledge and its own locally consistent behavior. Together, these factors substantially enhance training stability and generation quality under extreme few-step settings. Fig. aalso demonstrates that our method substantially reduces the performance degradation associated with a decreasing number of inference steps. Moreover, our framework introduces a step-unified design: instead of training a separate distilled model for each desired step size, single student model trained with ASD can flexibly support multiple inference-step configurations at deployment. This property significantly improves practical usability, it removes the need for repetitive re-distillation, which is especially valuable in scenarios such as dynamically balancing speed and quality trade-offs and accommodating deployment across varied resource settings.
P2-gen
Larger gap:
Pa-gen
Pt Plggn
Smaller gap
Pregen
Poges Gencration distribution of the n-step distillation model
Figure 1: Conceptual illustration of the different alignment strategies of DMD and ASD during dis- tillation. The points plotted in the figure indicate multiple modes (or peaks) of the real data dis- tribution, highlighting its multimodal nature.
To further improve video fidelity, we first conduct empirical analysis and observe that later frames exhibit higher generative redundancy compared the first frame. Motivated by this, we propose a frame-wise inference strategy with varying denoising strengths. Unlike previous methods that treat all frames equally, we allocate more denoising steps the crucial initial frames mitigate егrоr accumulation, while later frames are generated with larger skipping steps. This First-Frame Enhancement (FFE) strategy maintains low overall computational cost while notably improving visual quality. Extensive experiments on VBench demonstrate that our method surpasses state-of-the art approaches both one-step and two-step video generation. Importantly, it achieves both efficiency and flexibility by using single distilled model to support wide range of inference settings.
In summary, our contributions are as follows:
We propose an adversarial self-distillation strategy that aligns prediction distributions across different denoising steps of the student, significantly improving few-step generation quality.
We propose frame-wist inference strategy that allocates more denoising steps crucial initial frames, reducing error accumulation and improving video fidelity.
Our experiments demonstrate that our method surpasses the state-of-the-arr in few-step generation quality while eliminating the need for separate distillation training for each desired step size.
2 RELATED WORK
2.1
AUTOREGRESSIVE/DDIFUUSS VIDEO GENERATION
Diffusion Blattmann et al. 2023 Ho et al.
2022
Yang et al. 2025) and autoregressive models (Ge
al. 2022 Kondratyuk al."
2024 Yu et
al.
2024) are the two dominant paradigms in video
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4-step
Self Forcing
2-step
1-step
4-step
Ours
2-step
1-step
Fss
22.5
F5s
0=
1
1=5s
Figure 2: Qualitative results of Self Forcing (Huang et al. 2025) and ours under 4-step, 2-step and 1-step generation. Our method consistently maintains high-quality generation across 4-step, step, and -teep inference.
generation due to their ability to produce high-quality results. Diffusion models employ bidirectional attention (Bao et al. 2022 Peebles Xie| 2023) to denoise and synthesize all frames simultaneously. achieving strong temporal consistency but preventing frame-wise editing interactive generation. In contrast, autoregressive models generate frames sequentially by predicting the next token conditioned on previously generated frames. However, this strong dependence earlier outputs often leads to error accumulation, making it difficult to synthesize highly realistic long videos (Che et al. 2025).
Recent works (Chen et al. 2024 Hu et al.) 2025) Jin al., 2025 Li al. 2025 Yin al. 2025 Weng
iet al. 2024) have proposed hybrid frameworks that integrate diffusion and autoregressive generation. common design is model temporal dynamics autoregressively while applying diffusion- iterative denoising spatially. Despite their improved flexibility, their sequential and iterative nature introduces more complex generation processes and prolonged inference times. Building upon this line of research, our work adopts distillation-bast framework that significantly reduces generation latency, enhancing real-time interactivity in video synthesis.
2.2 ADVERSARIAL DISTILLATION
line of work (Song al. 2023 Song Dhariwal 2024 Liu et al. 2022 Meng et al. 2023 Berthelot et al.) 2023) focuses on distilling the multi-step generation process into a few steps improve the sampling efficiency of diffusion models. These methods train student model to approximate the ordinary differential equation (ODE) trajectory of the original teacher model. Adversarial distillation uses discriminator align the distribution of the student model with the target distribution. ADD (Sauer et al. 2024b) and UFOGen (Xu et al. 2024) aim for the student model's 's final output to be indistinguishable from real data.
Further advancements have sought to preserve the generation trajectory itself, not just the final output. SDXL-Lightning (Lin et al. 2024) and LADD (Sauer et al., (0244) achieve this by aligning the intermediate denoising states of the student model with those of the teacher model. Zhang al. (2024) and et al. (2025) fine-tune a pre-trained diffusion video model for AR generation via adversarial training, but they directly align sne-step outputs with real data. Our work employs discriminator to align the generated results of an n-step denoising process with those of an (n + 1) step process. This approach enhances the model's performance and consistency across various limited-step scenarios.
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denoise
Pn+1
n+1 steps
random noise
G
Dn
denoise n stops
Pn
gradient
backpropagation
DMD
loss
Figure 3: Pipeline of our proposed adversarial deff-distillation process. We employ discriminator D, to align the randomly noised n-step video with the (n++1 J-step one through calculating the ASD loss Equation (4) The generator G is optimized using a combined objective function that includes the standard DMD loss and the ASD loss. Note that distillation is performed in the latent space, while the pixel domain is primarily used for visual analysis and display.
2.3 SCORE DISTILLATION
Existing methods (Zhou al. 2024 et al. 2025) utilize score-based models to achieve distribution
matching across different noise states during distillation. For example, Dreamfusion (Poole et al. 2023) and Preaerr (Wang et al. 2023) leverage T2I diffusion model's score function to guide text-conditioned 3D generation. Distribution Matching Distillation (DMD) (Yin et al. 2024bb a) extends this approach to accelerate diffusion model generation by employing the original model as score estimator for real data and fine-tuned version score estimator for generated data.
Our approach is similar in that aims to maintain a consistent sample probability across distributions under particular noise level. To achieve this, align the noisy version of the predictions from our few-step model's n-step and (n++1 Jstepp denoising.
METHODOLOGY
To achieve high-quality and flexible causal video generation with minimal computational overhead, our distillation process employs two key strategies. We use Distribution Matching Distillation (DMD) to distill multi-step generative model into few-step student model through score-based alignment. Meantime, we introduce novel form of self-supervision by aligning the student model's n-step denoising with its (n-1)--tepp version in the distribution level. This dual supervision not only enhances the generation quality but also enables the final model to produce consistent, hight-quality output across variety of few-step settings. The few-step generator, the generator's score estimator and the discriminator are trained alternately to facilitate this process. The pipeline of the Adversarial Self-Do celf-Distillation (ASD) process is depicted in Fig. During inference, we further improve efficiency and quality by employing First-Frame Enhancement (FFE) strategy, which uses more intensive denoising process for the initial frame while reducing the number of steps for subsequent frames.
3.1 PRELIMINARY: DISTRIBUTION MATCHING DISTILLATION
Distribution Matching Distillation (DMD) YYn al. 2024a by presents a score-based approach to align a few-step student model with multi-step teacher model. Unlike other distillation meth- ods (Song et al.] 2023 Song & Dhariwal 2024). DMD not only accelerates generation but also improves output quality by aligning the output distributions. In diffusion models, the score function represented by the gradient of the log probability of the distribution:
s0(21, t) Vz, log logp(z.) <0(2;,
(1)
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Algorithm 1 Adversarial Self-Distillation Process
Require:
Few-step denoising steps T = {fi...., tx J. score function of teacher model 8dam Initialize few-step student model G. with the original model
1:
Initialize generator's 's score function Syen with Sima estimated by original model Initialize discriminator De with original model with trainable heads, D and Sgen share same backbone parameters
4: while training do
5:
Randomlnteger((1.N)
6: Predict few-step student model generate sample Ty +1 = Gy+ (21), (1 ((0
Add noise i
t) + te, e (0, 1),1 Uniform(7)
8:
Predict few step student model generate sample 26 G6 (z2), #2 (0,
9:
Add noise 2 = (1 t)æ² + tc, é (0,
10:
Update few-step generator Gg using DMD loss and ASD loss
Eq.
11:
Update discriminator D with ASD loss
Eq.
12: Update generator's function 8lm using diffusion loss Len
Eq
end while
where D; is the noisy sample at timestep t with p(æ;) as its corresponding data distribution, E (0..., T). p(ær) is a standard Gaussian distribution. ca( L) the predicted output of the generative model parameterized by 0 and o is predefined by the noise schedule (Karras et al. 2022 Song al., 2222b)) DMD minimizes the reverse Kullback-Leibler (KL) divergence between the score of the true data distribution and the score of the student model's generated data distribution. The gradient of DMD loss can be represented by:
VgLDMD - Egg (VeKL (Pgen, ())aaa,))
dGp(zz)
E [[saata (æe, t) Sgen (x t)) d0
(2)
2,f,0;
where daata and Sgen the score functions that trained of real data and generated data, which point towards the higher density of data for Pgen. and Paata.t- The noisy sample æ, is obtained by the diffusion forwarding process W, agdo Tje, E~ (0, I), where ig is the generated output of the few-step student model Gg. Cg, or > O are defined by the noise schedule. ~ N/O, I) is the Gaussian distribution.
The true data score Sdata is estimated by the original teacher model, while the generation score Sgen is estimated by a "teaching assistant" (TA) model, which is fine-tuned version of the teacher. The TA model and the few-step generator are typically trained in an alternating fashion. The TA model is fine-tuned on the standard diffusion denoising loss on the generated data from the student model:
ggn
c (1e, 1)
3.2 ADVERSARIAL SELF-DISTILLATION
key drawback of DMD is its lack of flexibility. The resulting student model is optimized for single, fixed number of steps, requiring separate distillation for each desired configuration (e.g., steps vs. 2 steps). To overcome this rigidity, we introduce a method that aligns the outputs single few-step model across varying step counts.
Our core idea is to align the n-step denoising distribution with the (n + 1)-step version. This ensures that the model can maintain consistent, high-quality output regardless of the chosen few-step setting. Since adversarial distillation objectives (Sauer et al., 2024b] [in et al., (024) are known to preserve sharpness and fine details better than DMD under low-step constraints, this alignment is performed using a discriminator D This discriminator's 's task is to make the outputs adjacent denoising steps indistinguishable through relativistic pairing GAN objective Martineu 2018). The adversarial self-distillation loss represented as:
LASD v)
E
[ (D) (V (2. D3(V (22))))
(0,1) =0,A
where G tries to maxmize LASD and D is optimizied minimize it. f(t) - log (1 + e-) is drawn from classic GAN (Goodfellow al. 2020 Nowozin al. 2016) and v represents the adding
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Algorithm 2 Inference Procedure with First-Frame Enhancement
Require:
Intensive denoising timesteps (f1,...1 fr reduced denosing timestops {ta,..., "aa...,,tx. tR), vidco length L, few-step autoregressive video generator Gø,
1: for L do Initialize: 1)
T
ifi=1
3: K
R
otherwise
4: for j j= to K do
5:
Predict 20
Go (t)
6:
Update x'' (8). e, ty41), where (0,
7: end for
8:
end for
9:
Return (
noise process. GB and Gz are the generated output of the few-step generator with n steps and (n++1) steps.
The few-step generator is trained with both DMD loss and ASD loss:
Liotal LDMD + a * LASD,
(S)
where a is the hyperparameter that weights the two loss functions. The detailed distillation process is shown in Alg. [
Crucially, aligning the outputs of two adjacent ftw-step predictions is a much more stable training objective than directly aligning few-step model with its multi-step teacher. The smaller distributional gap between adjacent steps makes our few-step generator significantly easier to train. This approach also provides a unique benefit: the n-step generator receives a supervision signal not only from the teacher model's 's trajectory but also from the (n++1--s 1)-step prediction, a novel form of self-distillation that further boosts the model's generation quality.
3.3 FIRST-FRAME ENHANCEMENT
Existing causal video generation methods typi- cally employ uniform number of denoising steps for each frame. However, causal generation pro- cess, the quality of subsequent frames is critically dependent on the preceding frames. The quality defects in earlier frames are highly likely to be propagated throughout the rest of the sequence. This is particularly true for the first frame, which has no prior context and must synthesize a high- quality initial state from zero-data starting point. Consequently, the generation of the first frame re- quires dedicated attention mitigate accumulated етror and ensure high overall video quality. Fig. ssowws the similarity of predicted 2. for different video frames across various denoising steps. It reveals that the first frame has low similarity across steps, indicating that each denoising step is crucial. In contrast, subsequent frames exhibit higher similarity, suggesting greater redundancy that makes them more suitable for a few-step prediction. These results are based on the Self Forcing model and are consistent across multiple prompt variants and random seeds. We include additional results across scenarios in the Appendix Scction C
The similarit matrix predicted under dilferent denoising
Frame Index
Figure 4: Cosine similarity matrices of differ- ent frames in causal diffusion video generation. Each matrix shows the similarity of the predicted do between different denoise steps (from to 4).
Based this observation, we propose novel First-Frame Enhancement (FFE) denoising strategy, as detailed in Alg. 2 The first frame undergoes a more intensive denoising process, requiring a minimum of four steps, while subsequent frames can be generated with significantly reduced number of steps, such as one or two. This frame-based control allows us to enhance the quality of the generated video in the few-step scenario.

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Published as a conference paper at ICLR 2026
Wan 2.1
Skyreels
Vid
Self Forcing
Ours
1=
2225s
55
0-
2-25s
f=$s
Figure Qualitative comparisons. We visualize videos generated by Ours against those by 1 (Wang al. 2025). SkyReels-V2 V2 (Chen et al. 2025), Caus Yin et al. 2025) and Self Forcing (Huang et al. 2025) at 4-step generation. All models share the same architecture with 1.3B parameters.
4 EXPERIMENTS
4.1
IMPLEMENTATIONS
Our approach employs a causal video distillation framework based on the Self Forcing (Huang et al. 2025) training paradigm. The causal model architecture builds upon the Wan2.1-T2V-1.. (Wang et al.
2025) backbone, Flow Matching based model (Lipman et al. 2023). We adopt Caus Vid's (Yin al.
2025 initialization protocol to stabilize early causal training phases via asymmetric distillation
from pre-trained bidirectional teacher model. For training data, utilize the exact text prompts from Self Forcing for fair comparison. The diffusion process is optimized with a 4-step denoising schedule during training, while using step-skipping strategy during inference to accelerate generation. Adversarial training objective is incorporated via integration of RpGAN (Jolicoeur) Martineau 2019) objectives with RI (Mescheder et al. 2018) and R2 regularization terms following R3GAN (Huang et al.) 2024a). And we use the frozen fake score function as the backbone of the discriminator following DMD2 Yin al. (0024)). To reduce computational cost and the number of parameters, we model discriminators D output logits as the n-th dimensional output logit of the final layer, thus different n share the same backbone and classifier head. The performance is rigorously assessed using VBench (Huang et al. (024b) for multidimensional evaluation and user preference studies to quantify numan-perceived visual quality and semantic alignment.
4.2 COMPARISON WITH EXISTING BASELINES
User Preference Study
Following the protocol of Self-Forcing, we com- pare with representative open-source text-to-video models under various inference steps, ranging from 64 4, ensuring comprehensive assess-
Ours (4)
67.0%
33.0%
MAGI-1 (64)
(4)
79.0%
21.0%
Ours (4)
78.0%
22.0 0%
Skyreel-V2 (30)
ment. For fair comparison under 2-step and 1- step generation, we additionally train 2-step and 1-step distilled versions of Self-Forcing and eval- uate them against our model. As shown in Tab. [] our model achieves slightly better performance compared to Self-Forcing under 4-step generation. Notably, in both the 2- step and 1-step settings, approach outperforms the specifically distilled ver- sions of Self- Self-Foreing without requiring additional
Ours (4)
85.0%
5.0%
CausVid (4)
Ours (4)
50.0%
50.0%
Self Forcing (4)
Ours (2)
81.0%
19.0%
Self Forcing (2)
Ours (1)
98.0%
Self Forcing (1)
Preference Rate (%)
Figure 6: User preference study. "(n)" - num- ber of denoising steps. denotes the retrained version under 1 & 2 step settings.
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Table 1: Comparison with relevant baselines. We compare our method with representative open- source video generation models of similar parameter sizes and resolutions. The n-step generation with FFE strategy is denoted with n". denotes the retrained version under & step settings.
Evaluation scores 1
Model
#Params
Resolution
Denoising Steps.
Total Score
Quality Score
Semantic
Score
Many Steps
MAGI-1 Sand-A 2025
4.5B
882x480
64
79.18
82.04
67.74
Wan2.1 Wang ct al. 2025
1.3B
832x480
50
84.26
85,30
80.09
SkyReels- V2 Chen ct al.
2025
1.3B
960x540
30
82.67
84.70
74.53
NOVA (Deng al. 2025
0.6B
768 480
25
80.12
80.39
79.05
LTX-Video (HaC ohen
2024
1.9B
718x512
20
80.00
82.30
70.79
Pyramid Flow (Jin el al. 2025)
2B
640x381
20
81.72
84.74
69.62
Steps
Caus Vid Yin et al. 2025
1.3B
832
4
81.20
84,05
69.80
Self Forcing (Huang ct al.
202.5
1.3B
832x 480
4
84.31
85.07
81.28
Ours
1.3B
832 480
4
84.38
85.16
81.25
2 Steps
Self Forcing' Huang et al.
2025
1.3B
832 480
83.49
84,20
80.62
Ours
1.3B
832x 480
2"
84.32
85.15
81.02
Steps
Self Forcing'
(Haag et al. 2025
L.3B
832 480
1
80.62
81.19 84.55
78.35
Ours
1.3B
832x 480
83.89
81.24
parameter optimization. For instance, under 1-step inference, our model exceeds Self-Forcing by 3.27 points in Total Score.
As illustrated in Fig. 5 our method capable of generating high-fidelity videos using only approxi- mately 8% and 13% of the denoising steps required by Wan 2(ang al. 2025) and SkyReels(Chen ct al. 2025). respectively, while achieving visually superior results. This leads to a substantial reduc- tion computational cost. Moreover, under the same number of denoising steps, our approach yields improved detail and visual quality compared to Caus et al.] 2025) and better video details al.) 2025). Fig. shows the user study results comparing our
compared Self-forcie forcing(Huan) model against several important baselines. Our approach is consistently preferred over many-step baselines, including the many-step Wan2.1 1 that our model is initialized from. Meanwhile, our results are 70% better than Caus Vid (Yin et al. (025) and on par with Self Forcing, since both our model and Self Foreing use identical DMDD-based supervision the 4-step setting. For the extreme 1-step and 2-step generation setting, method yields significantly 96% and 62% preference over Self- Forcing, respectively. This demonstrates the effectiveness our proposed ASD and FFE strategy in improving extremely few-step video generation.
4.3 ABLATION STUDY
To better understand the contribution of each component to skip-step generation, we conducted a comprehensive ablation study, shown in Tab. As shown in the first two rows, using identical training data, our ASD training method consistently improves the quality of both 1-step and 2-step generation during inference. For example, it raises the Total Score by 2.52 and the Semantic Score by 6.84 under one-step generation. Comparison between the first and third rows reveals that the FFE inference strategy leads to substantial gains in overall video quality-
a increase in Total Score and 10.64 improvement in Semantic Score for one-step generation, even exceeding the quality of the original two-step generation results (row 1). Morcover, combining both methodologics further enhances performance, demonstrated in the last row.
We qualitatively compare the quality of generated outputs with and without our proposed ASD training and FFE inference strategies, illustrated in Fig. 7 For simplicity, n-step generation with the FFE inference strategy is denoted as n' -step. When adopting the ASD training strategy, the generation quality at 2" -see is comparable to that achieved with 4 steps, while even under the
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Ours
Ours w/o ASD
4 steps
2 steps
1* steps
2 steps
step
[=0s
2=258
5=
t=s
2=258
1=88
Figure 7: Qualitative comparison illustrating the effects training and inference strategies on video generation. Left: results using the ASD training method; right: results without it. For simplicity, inference with the FFE strategy is denoted with
Table 2: Ablation Study. The symbols v and x denote the inclusion and exclusion of the correspond- ing strategy, respectively. ASD refers to the Adversarial Self-Distillation strategy in Section 3.2 and FFE denotes the First-Frame Enhancement strategy introduced in Section 3.3
Methods
2-step Generation t
1-step Generation
ASD
FFE
Total Score
Quality Score
Semantic Score
Total Score
Quality Score
Semantic
Score
X
82.61
83.84
77.68
78.13
80.33
69.31
X
83.28
84.17
79.69
80.65
81.77
76.15
X
83.80
84.65
80.40
83.04
83.81
79.95
84.32
85.15
81.02
83.89
84.55
81.24
challenging 1 1t-step setting, video quality is well maintained. Morcover, at both 2-step and 1-step inference, the frame quality noticeably superior to variants trained without the ASD strategy. This shows the effectiveness of our ASD training strategy under skip-step generation. More specifically, the variant trained without the ASD strategy exhibits obvious error accumulation under the 2" setting, resulting in noticeable background shifts and character blurring condition, the severe blurring occurs as early as t= 2.5s. As demonstrated by the comparison between rows and rows 3 & the FFE inference strategy that increases the denoising steps for the initial frame substantially enhances overall video quality by effectively mitigating artifacts such as background and subject blurring in the first frame.
5s. And under the 1 1'-step
CONCLUSION
In this work, we introduce an Adversarial Self-Distillation training objective for causal video diffusion models' distillation, along with First-Frame Enhancement inference strategy for efficient sampling. The adversarial objective encourages the n-step generated video distribution to approximate that of the 11step generation, thereby progressively improving few-step generation quality. During inference, we explicitly distinguish between the first frame and subsequent frames in causal video generation, allocating different numbers of denoising steps enhance overall video quality with low computational overhead. Experiments demonstrate that our distilled model outperforms other baselines under 1- and 2-step generation configurations.

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6 REPRODUCIBILITY STATEMENT
A comprehensive description of the implementation is provided in Section 4.1 and Section A The source code and additional comparison videos are included in the supplementary material and will be released in the future.
ETHICS STATEMENT
This work focuses improving the efficiency of diffusion-baset video generation through distillation. Our contributions are methodological and technical in nature, and do not involve human subjects, personal or sensitive data, medical applications. The experiments available benchmark datasets (e.g.. VBench), which are widely used in prior research and do not contain personally identifiable information.
conducted on publicly
We confirm that this research adheres to the ICLR Code of Ethics and complies with all relevant institutional and legal standards. No conflicts of interest external sponsorship influencing the work are present.
8 ACKNOWLEDGEMENTS
This work supported by the National Natural Science Foundation of China under grant 62372341. The authors were partially supported by the Intelligent Computing Center of the National Cybersecu- rity Talent and Innovation Base, Wuhan.
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A DETAILS OF IMPLEMENTATIONS
Our implementation is largely based on the code from Self-Forcing(Huang al. 2025). Specifically, we adopt the chunk-wise DMD (Yin et al. 2024b) training variant of Self-Forcing, with all training conducted in the VAE latent space using a chunk size of 3. Following Self-Forcing, Wan 2.1 14B model as the real score estimator. The model is trained with a batch size of 8 for 3,000 steps.
utilize the
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For Adversarial Self-Distillation incorporate the R1 and R2 regularizations 2018) from the R3GAN (Huang et al. 2024a) objective.
Mescheder et al.
Leg = = E8
2 1,al"1 .e, (x) (x] fal + + II5 (x?) - fu(2 +o +
(6)
(D(V) - - mu? llg (sigmoid ( fb(x)))) X
Co( - -B, +,,,t 2 [log (sigmoid (ff() - fz(xz= 1)]]
(8)
where xt nt1 ~ po,n+l,ts xz ~ pa. are the noisy (n+ (a+l)-step generation data and n-step generation data, respectively, and are Gaussian noise sampled from NO, 1), and f is the critic network (discriminator) of GAN. We use = 600, a = 0.05 for all experiments. The generator, fake score estimator, and discriminator are optimized alternately with ratio of 1:4:1, following Yin al. 2024a Huang et al. 2025). Following Self-Forcing, we inserted additional cross-attention layers and classification heads (eevving discriminator heads) at layers 12, 21, and 29 of the fake score model. These discriminator heads operate solely on backbone features and query tokens, without access to the timestep This implementation detail will be explicitly included in the revised manuscript.
Note that we discard ASD Loss for the last step denoising. For simplicity, we reserve the index n of the n-th discriminator Dn that discriminates the n-step generation distribution from that of (n + stet here.
For the retrained Self-Foreing baselines, we use exactly the same hyperparameters as the official implementation, except for changing the student model's 's number of denoising step list from [1000, 750, 500, 250] to [1000, 500] or [1000]. Specifically, we used: real score CFG weight: 3.0, optimizers: AdamW for both generator and discriminator with 31 = O, Bg = 0.999, le-8, weight decay 0.01, learning rate (generator): 2e 6, learning rate (discriminator): 4e generator/diseriiinator update ratio: 5:1. Training is monitored until convergence.
B VBENCH SCORES ACROSS ALL DIMENSIONS
In Figure we evaluate our method and Self-Forcing across all 16 VBench metrics under both I-step and 2-step generation settings. Our method consistently outperforms Self Forcing, achieving notably higher scores in semantic and scene. Moreover, it demonstrates superior performance in dynamic modeling, as evidenced by significantly higher dynamic degree score.
cartinent-ptiicularyyyy in object class, multiple objects, spatial relationships,
C CAUSAL DENOISING TRAJECTORY SIMILARITY ACROSS SCENARIOS
apply the analysis both To further validate the generality of the phenomenon in Section 3.3
our method and Self-Forcing across three diverse scenarios: VBench Huang et al.) 2024b), complex object motion, and high camera motion. For each, we record denoising trajectories from 200 video samples, yielding Figure 9 The figure shows that, across all scenarios, the first frame exhibits low similarity across denoising steps, indicating each step is critical. In contrast, subsequent frames show high inter-step similarity, suggesting greater redundancy and thus greater suitability for few-step prediction. Moreover, our method achieves even higher similarity in later frames, demonstrating that ASD enables the student to match multi-step quality with fewer steps, effectively supporting step-skipping inference.
D REDUCED DISTRIBUTIONAL GAPS BETWEEN ADJACENT DENOISING STEPS
To empirically validate the smoother transitions between adjacent denoising steps, we conduct a quantitative analysis using the Fréchet Video Distance (FVD). Table E presents FVD values computed between outputs at step n and step n + 1 (i.e., adjacent steps), as well as between step n and the 50-step 14B teacher model.
As illustrated in Table 3 the FVD between adjacent denoising steps is consistently lower than that between each step and the final teacher output. For instance, step 1, the adjacent-step FVD is
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dynamic degree motion smoothness
aesthetic quality
Imaging quality
temporal flickering
object class
b kkrround consistency
multiple objects
pject consistency
human action
overall consistency
color
temporal style
spatial relationship
appearance style
Ours 1*tstep
Self Forcing 1-step
Self Forcingt 1-step Self Forcingt 2-step
2*-step
Self Forcing 2-step
Figure 8: VBench scores visualization. We compare results with Self Forcing Huang al. (2025) variants using all VBench metrics under 1-/2-step setting. The retrained 1-/2-step version denotes Self Forcingt.
Table 3: Fréchet Video Distance (FVD) between video distributions at different denoising steps.
Lower FVD values indicate smaller distributional gaps.
Comparison
Step 1
Step 2
Step
Step 4
n + n +1 732
1136
441
N/A
n + teacher
1836
1646
1454
1448
732, much smaller than 1836 for the teacher-step comparison. These results provide quantitative support that the distributional discrepancy between consecutive steps substantially smaller than the divergence from the teacher distribution. The Adjacent- Distribution (ASD) objective capitalizes on this property by aligning each step with its immediate successor, which exhibits lower distributional divergence. Overall, these findings corroborate the core assumption of our approach: transitions between adjacent denoising steps are indeed smoother.
E FEW-STEP LONG-VIDES GENERATION EVALUATION
To demonstrate that method enables high-quality, long-video generation with few (1 2) denoising steps-and effectively mitigates error accumulation -e generate 20-second continuations using both 1-step and 2-step inference for approach and Self-Forcing, and evaluate them VBench(Huang et al, 2024b).
As shown in Table our method overall outperforms Self-Forcing both quantitatively and qualitatively in long-video generation. For visual comparison, Figure 10 presents side-by-se bdde-by-side 2testep (ours) vs. 2-step (Self-Forcing) 20-second samples. Our results exhibit clearly superior performance in motion
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The similarity matrix of predicted & under different denoising steps.
Jos Poooooo 3
0.5
Bench
Framz Index
Ours
0.5
Complex Object Mov
Self Forcing
0.5
Frume
Ours
0.5
Self Forcing
High Camera Motion
0.5
Inles.
0.5
Figure 9: Cosine similarity matrices of predicted i'g denoising steps to 4) in causal diffusion video generation, comparing our method and Self-Forcing (Huang et al. 2025). We report results averaged separately for VBench, complex object motion, and high camera motion.
dynamics (rows 3, 4, 6), visual detail and fidelity (rows 1, 2, 3, 6), and camera motion realism (rows 2, 4).
These results confirm that ASD not only reduces computational cost also enhances temporal coherence and visual quality in extended video generation.
Table 4: VBench evaluation scores for our method vs. Self-Forcing under few-step long-video (20-second) inference settings
Method
Total Score
Quality Score
Semantic Score
Self Forcing Huang al.
2025 (2-step)
0,8250
0.8293
0.8076
Ours (2*-step)
0, 0,8263
0,8329
0.7998
Self Forcing(Huss al.
2025
(1-step)
0,8066
0.8101
0.7923
Ours (I (tsep)
0.8200
0.8248
0.801 1
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Self Forcing
Ours
Self Forcing
Ours
Self Forcing
Ours
Self Forcing
Ours
Self Forcing
Ours
Self Forcing
Ours
Figure 10: Our method versus Self- Solf--Frciigg uniformly sampled frames (including first and last) from 20-second long videos.
F HYPERPARAMETER ANALYSIS
To investigate the impact of the ASD loss on model performance, we evaluated different variants of the hyperparameter the Equation (5) and computed the corresponding Total Score on VBench under three inference settings, as illustrated in Fig The results indicate that at 4-step generation, the Total Scores across different model variants are comparable. When using 2" stee generation, the variant without ASD Loss (a 0) exhibits noticeable performance drop, while other variants maintain performance levels similar to those observed 4-step generation. Under 1 -tet generation, all methods experience decline in performance, with the most pronounced degradation occurring when a is set to zero. These findings demonstrate that the proposed ASD training objective enhances video quality under skip generation settings and remains robust across wide range of hyperparameter
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a-0
o=10
x=20
0=30
Total Score
0.83
0.82
0.81
0.80
2
Denoising Steps
Figure 11: Sensitivity analysis. Impact of parameter in Equation 5 the VBench(Huang al. 2024b) Total Score across three inference scenarios. For simplicity, inference with first-frame enhancement is denoted with
values. While absolute values vary slightly across a, the trend is consistent: skip-step performance improves markedly whenever O.
G USER STUDY DETAILS
We randomly selected 20 prompts from the VBench Huang et al. 2024b) prompts expanded by Self-Forcing. Five baseline methods Sand-Al (205);; Huang et al. (2025); Yin et al. (2025); Chen al. (2025); Wang et al. (2025 were used generate corresponding videos, which were then paired with corresponding videos generated by our method, resulting in 5 x 20 video pairs. During the user study, each participant (a total of 12 participants) evaluated 20 video pairs, including 4 randomly selected pairs from each baseline. The order and pairing of videos were independently randomized for every participant. Participants could not each other's choices. The final results across all participants. The user study interface, as shown in Figure T2 displayed a video pair alongside the corresponding prompt and selection buttons. Participants could choose whether the left video was better, the right video was better, or both were of similar quality. To ensure a fair comparison with both one-step and two-step configurations, we retrained the one-step and two-step versions of Self-Fou self-Forcing.
averaged
H USAGE OF LLMS
In this work, LLMs were primarily used to assist in grammar checking. All outputs from LLMs were manually reviewed.
I TRAINING DYNAMICS ANALYSIS
lngit diff
Figure 13 presents the DMD loss curves of our method and Self-Foreing during training. Notably, our approach exhibits significantly re- duced fluctuations in DMD loss compared to Self-Forcing. With ASD, the DMD loss shows lower mean 18. vs. 0. 1989) and dramatically reduced variance (0.00258 vs. 0.01963) com- pared to pure DMD training, demonstrating that the proposed ASD loss effectively stabilizes dis- tribution matching with the teacher model dur- ing training.
D106
.000
Figure 14: The discriminator's predicted logit dif- ference between n-step and (n+1)-step samples during training.
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Prompt: bicycle accelerating to gain speed, captured smooth time-lapse sequence. bicycle a classic red road bike with sleek aerodynamic handlebars and reflective wheels. The rider, focused individual in cycling gear, leans forward, pedaling vigorously. The background shows a winding asphalt road leading into forest, with trees and bushes blurring past the bike speeds up. The camera starts from mid-shot and gradually zooms in to focus the rider's determined face and the spinning wheels, emphasizing the acceleration.
0:0: 025
Meck ds think bettert
Right
Equally good
Nest
Previcus
Figure 12: User study interface. Participants were asked to select the higher-quality video in each pair to indicate if the two were of similar quality.
Loss
4.6
DND
0.5
16
66
16
500
155
(a) Self Forcing
(b) Ours
Figure 13: The DMD loss curves of Self Forcing and method during training. The DMD loss values remain around 0.2 for both methods, but our method exhibits substantially lower fluctuation.
Figure 14 shows that the discriminator's logit difference between n-step and (n+1)-step samples oscillates calmly around zero, indicating that the generator and discriminator co-evolve stably rather than collapsing or diverging. This behavior supports that ASD training is stable in practice, despite using adversarial term.
Training Overhead.
The ASD framework introduces only a lightweight discriminator on top of the existing TA score model's backbone, using shared parameters for all n-step discriminators. This results in minimal extra memory usage compared to traditional DMD. The additional GPU memory required by ASD is +1.3% compared to the Self-Forcing baseline, which is modest increase. The additional training time is approximately +20% relative to Self- Self-Forcing. Given that ASD allows single model handle multiple inference steps (1, 2, 3, 4), this overhead is relatively small compared to the computational cost of training separate models for each step.
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Reviews:
[{"review_id": "scAQ8s35qb", "reviewer": "Reviewer_LHmL", "summary": "This paper proposes a framework for accelerating causal video diffusion models via Adversarial Self-Distillation (ASD) and First-Frame Enhancement (FFE). The method extends Distribution Matching Distillation (DMD) by introducing a discriminator that aligns the student model’s n-step and (n+1)-step denoising distributions, instead of aligning directly with a multi-step teacher. This step-wise self-alignment aims to stabilize training under extreme few-step (1–2 step) scenarios. Additionally, FFE allocates more denoising steps to the first frame to mitigate error propagation in causal video generation. Experiments on VBench show that the proposed model surpasses Self-Forcing and CausVid under 1-step and 2-step configurations, while achieving comparable performance to multi-step baselines such as Wan2.1 and SkyReels with much fewer steps.", "questions": "1. The proposed FFE seems to benefit early generation more, right? Can you provide more results and comparisons for longer generations, such as 10s-20s level? \n\n2. In the user study, the proposed method is on par with Self-Forcing at the 4-step setting (exactly 50%). Can you elaborate more on why ASD is beneficial at 1-2 steps (as in the ablation) but not helpful in the 4-step setting?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The paper is clearly written and easy to follow. The proposed methods (ASD and FFE) are well-motivated and clearly elaborated. \n\n2. The results look very impressive. Compared to the Self-Forcing baseline, the 1-step video generation exhibits a great boost in quality. The speed of 1-step causal generation will enable a wider deployment of streaming video generation.", "weaknesses": "One minor concern might be conceptual novelty. The main method of this work, ASD, is not fundamentally new [1,2]. \nConsidering the value and impact of 1-step causal video generation, the engineering effort to tune an end-to-end pipeline is a significant contribution, especially that the authors provide the code for replication in the supplementary material. \n\n\n[1] Zhang et al., SF-V: Single Forward Video Generation Model. \n\n[2] Lin et al., Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation.", "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": "SszRJToEFw", "reviewer": "Reviewer_MC9Q", "summary": "This paper works towards limiting the diffusion denoising steps of hybrid video generation frameworks, that leverage autoregressive models to model temporal dynamics and diffusion-based spatial denoising, to as few as one step. The proposed method falls under the category of model distillation and strongly builds on an existing method called Distribution Matching Distillation with a substantial addition: A novel form of Adversarial Self Distillation is proposed, aligning the student model’s n-step denoising process with its (n+1)-step version on a distribution level. Results on VBench and a custom user study show state of the art results on 1 and 2 step video generation. The model further removes the limitation of fixed inference steps after training, allowing flexibility for multi-step settings.", "questions": "- Eq. 2: isn’t there a bracket missing?\n- How is Fig. 4 created? Is this just an example or averaged results? Are the results corresponding to results from Self Forcing or from the provided method?\n- Regarding user preference study: Please provide more details on e.g. how many users were selected, were measures taken to ensure independence?\n- In the ablation Table 2: Why are the total scores for the first row (ASD, FFE) so much worse than the corresponding pure Self Force values from Table 1?\n- The distilled diffusion process is optimized with a 4-step denoising process. Was this number of steps determined to optimal?\n- Influence of $\\alpha$: Figure 8 already shows influence of alpha on the Total Score. However it is interesting to see that the order of the alpha values with increasing scores (for 2 or 1 step) goes with: alpha=0 < alpha=20 < alpha=10 < alpha=30, which shows a non-monotonuous increase of the total score with increasing alpha. How do you explain this behavior?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is generally well written and presented.\n- Provided 1 and 2 step video results, both in the paper and supplementary material, show better results than current competitors.\n- The method supports both single and few / multi step inference which is a major advantage over fixed step trained methods\n- The observation First Frame Strategy seems to be an important observation, by itself already boosting state of the art results\n- The influence of ASD and FFE cleanly ablated showing the superior results of the combination", "weaknesses": "## Incremental contribution:\n- The used components are not fundamentally new in nature. DMD is very well established for model distillation and remains a core component also in this work. \n- Similar adversarial diffusion distillation has been proposed before and is well established in the community\n- As a such there are no fundamentally new concepts presented, but their combination provides a nice contribution to the current state of research.\n\n## Limited information on experiments:\n- There are several information missing on some of the shown experiments\n- The user study is missing the number of participants and further statistical values\n- For the main comparison for 1 or 2 step distillation, the authors had to retrain Self Forcing. Retraining parameters are missing i.e. indicating that the model has been trained long enough to convergence\n\n## Figure 1:\n- Figure 1 seems not really on point, i.e. the adversarial self-destillation on the right seems oversimplified and not aligned with Algorithm 1. \n\n## Minor:\n- Algorithm 1, 1: Typo: origianl", "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": "OwnaKIbK1U", "reviewer": "Reviewer_wWot", "summary": "This paper addresses the challenges of slow inference speed and error accumulation in causal video generation models. The primary goal is to enable high-quality video synthesis in extremely few denoising steps, the authors propose two main contributions:\n1. Adversarial Self-Distillation (ASD): A novel strategy that moves away from traditional distillation. Instead of matching a few-step student model to a multi-step teacher model, ASD adversarially aligns the output distribution of the model's own n-step generation with its (n+1)-step generation.\n2. First-Frame Enhancement (FFE): An inference strategy designed to mitigate error propagation. Based on the observation that the initial frame is most critical in causal generation, FFE allocates more denoising steps to the first frame and significantly fewer steps to subsequent ones, enhancing overall video quality with minimal computational overhead.", "questions": "1. The central premise of ASD is that the \"intra-student gap\" is smaller and easier to bridge than the \"teacher-student gap.\" Could you provide a more formal or empirical justification for this claim? For instance, have you measured this \"gap\" using any distribution divergence metrics (e.g., KL, Wasserstein) to validate this core assumption?\n2. Given the known training instabilities of GANs, the claim that ASD provides \"smoother supervision\" is counter-intuitive. Could you elaborate on this and provide evidence (e.g., loss curves, gradient norm analysis) to support that the adversarial self-distillation process is indeed more stable than direct distillation from a fixed teacher?\n3. The self-distillation mechanism seems to carry an inherent risk of error reinforcement, where the model could amplify its own artifacts over time. How does the framework explicitly guard against this \"model drift\"? What is the role of the DMD loss in anchoring the training?\n4. The training procedure appears significantly more complex than the baseline. Could you provide a more transparent comparison of the training time, computational resources, and overall stability of your method compared to the standard distillation approach used to train the Self-Forcing† baseline?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Well-Motivated and Significant Problem: The paper addresses a highly relevant and challenging problem in generative AI: achieving high-fidelity video synthesis under extreme computational constraints (one or two inference steps). The motivation is clear, and a successful solution to this problem would have a significant practical impact, making the research direction valuable.\n2. Empirically Effective and Intuitive Core Ideas:\na) ASD's Practical Efficacy: The core idea of Adversarial Self-Distillation (ASD)—aligning the model's n-step output with its (n+1)-step counterpart—is an intuitive approach to breaking down a large distillation gap. While its theoretical underpinnings could be further explored, its practical effectiveness is undeniably demonstrated by the experiments. The concept of progressively refining the model based on its own slightly improved outputs proves to be a powerful empirical strategy in the few-step regime.\nb) Pragmatic and Data-Driven FFE: The First-Frame Enhancement (FFE) strategy is a pragmatic and effective solution grounded in a clear empirical observation (Figure 4). Although a simple heuristic, it demonstrates a thoughtful consideration of the error propagation dynamics in causal models. This data-driven approach to allocating computational resources where they are most needed is a clever and impactful inference-time optimization.\n3. Rigorous and Comprehensive Experimental Validation: \na) Strong and Fair Baseline Construction: The authors' decision to train their own few-step versions of a powerful SOTA model (Self-Forcing) for comparison is a sign of rigorous scientific practice. This \"apples-to-apples\" comparison effectively isolates the contribution of their proposed methods (ASD and FFE) from confounding variables like model architecture, which makes the reported gains highly credible.\nb) State-of-the-Art Empirical Performance: The paper presents compelling quantitative and qualitative results that convincingly demonstrate state-of-the-art performance in the challenging one- and two-step video generation tasks. The significant lead over a fairly-trained baseline, supported by extensive ablation studies (Table 2) and a strong user preference study (Figure 6), provides undeniable proof of the method's empirical superiority.\n4. Clarity and High-Quality Presentation: The paper is well-written, clearly structured, and easy to follow. The figures and tables are informative and effectively communicate the core concepts and results, contributing to a high-quality presentation of the work.", "weaknesses": "1. Methodological Foundation of ASD Lacks Rigor: The central contribution, Adversarial Self-Distillation (ASD), is built on a foundation that is more intuitive than it is rigorous. The paper's core claims—that the \"intra-student gap\" is smaller and that adversarial alignment provides \"smoother supervision\"—are presented as assertions rather than demonstrated principles.\na) There is no formal analysis or empirical measurement to quantify this \"gap\" (e.g., in terms of a specific distribution divergence metric).\nb) The claim of \"smoother supervision\" from a GAN objective is counter-intuitive, given the well-documented instability of adversarial training. The paper fails to provide evidence to substantiate why this would be the case, especially compared to simpler, more stable alignment objectives.\n2. Unaddressed Risk of Error Reinforcement in Self-Distillation: The ASD mechanism, where the model learns from a slightly better version of itself, introduces a significant and unexamined risk of \"model drift\" or \"error reinforcement.\" If the (n+1)-step generation is flawed (e.g., contains artifacts or mode collapse), ASD could perversely train the n-step model to replicate these very flaws. The paper relies on the DMD loss to anchor the model to the true data distribution but provides no analysis of the training dynamics or the delicate balance required to prevent the self-distillation objective from amplifying its own mistakes.\n3. The FFE Strategy is Heuristic and Its Generalizability is Questionable: The First-Frame Enhancement (FFE) strategy is presented as a key contribution, but it is fundamentally an empirically-driven heuristic rather than a principled method.\na) Its justification rests entirely on a single observation on a specific dataset (Figure 4), and its generalizability to different video content (e.g., videos with major mid-sequence scene changes) is not explored.\nb) The paper completely ignores the potential negative side-effects of this strategy. Creating a sharp drop in denoising steps between the first and second frames could introduce significant temporal discontinuity and artifacts, undermining the very quality it aims to enhance. This critical aspect is neither analyzed nor discussed.\n4. Insufficient Discussion on Training Complexity and Stability: The proposed training framework is remarkably complex, involving a generator, a discriminator, and a \"teaching assistant\" (TA) score model trained in an alternating fashion. The paper largely overlooks the significant practical challenges this entails. There is no discussion of the training stability, the sensitivity to the delicate balance of multiple loss terms and optimizers, or the total computational overhead of this complex setup compared to simpler distillation baselines. For a paper focused on efficiency, the lack of transparency regarding its own training costs is a notable omission.", "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": "t7DT1QQTUQ", "reviewer": "Reviewer_nsqN", "summary": "The authors propose a method for the distillation of bidirectional video contraption models into autoregressive causal few-steps generators capable of real time inference, basing their work on the popular Self Forcing framework. The paper introduces two advancements with respect to Self Forcing: 1) it enables performing inference with a variable number of sampling steps (1, 2, 4); 2); It improves performance for the case of 2 and 1 step inference with respect to Self Forcing, producing 2 step generation results on par with the original 4 step inference according to VBench scores. The technical contribution enabling these advancements consist of: 1) an adversarial loss term matching N+1 with N step generator distributions; 2) forcing the first frame to be inferred using 4 steps. Both contributions appear sound and easy to implement (authors provide code in the supplement). The quality of the results in convincing as seen qualitatively and in VBench and user studies. Overall, the work is likely to be adopted by the community due to its simplicity, convincing quality of the results and relevance of the problem.", "questions": "- An adversarial term is introduced to match distribution between N+1 and N step predictions, showing performance improvement. Did the authors consider extending use of the same adversarial term to its canonical usage for matching the real data distribution with the 4 step generator distribution similarly to DMDv2?\n- Adversarial losses are proposed as the tool for matching the N+1 and N step generator distributions. Why usage of an adversarial term is the ideal choice in this context? Could we have used a DMD formulation instead by introducing additional fake score prediction networks, either one for each value of n, or sharing the same fake score prediction network with conditioning on n? This could result in a more elegant framework only relying on DMD.\n- FFE relies on the assumption that generation of the first frame is the hardest, because successive frames can be generated by copying content from the first frame. Thus allocating more sampling steps to the first and less to the subsequent ones makes sense and is shown to improve performance. The assumption however holds less strongly if videos with high camera motion or complex object movements are considered. In this setting each frame will need to generate a more significant amount of content without possibility for copying it from previous frames. Can the authors show that in this setting, their method with 2 steps inference is still matching performance of the original 4 steps Self Forcing?\n\n\n- Algorithm 1: LL217 shows that a schedule with a fixed number of steps N is instantiated. LL222 suggests that the actual number of sampling steps for the current iteration n is sampled. I believe LL217 should instead instantiate a different schedule for each possible value of n\n- Algorithm 1: ll224 suggest a rectified flow setting. I suggest making this explicit in the paper\n- Algorithm 1: LL224 LL226 suggest two different time steps are sampled for x^1 and x^2 for use in the adversarial loss term. I'd like to confirm this understanding is correct. Could the authors discuss why this is preferable to having a shared timestep t to ease the role of the discriminator?\n- LL352-355 are unclear. How is the discriminator implemented? Does the discriminator receive as input the current timestep t in addition to backbone features? D_n and D^n seem to be used interchangeably\n- Eq 2 has incorrect parenthesis\n- Could authors report all VBench evaluation metrics in the supplement?\n- How is Fig 1 produced? Did the authors perform experimentation on a gaussian mixture?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The work tackles the task of making slow bidirectional video models, fast real-time autoregressive generators which if of high practical importance\n- The quality of results is convincing as seen in the provided qualitative samples\n- Quantitative evaluation confirms qualitative assessment that the method matches or surpasses Self Forcing under the same amount of sampling steps\n- The method is simple to implement, and authors provide the source code for review\n- Ablation studies show convincingly that ADS and FFE are both having a positive impact on the method and ablations on the optimal value for adversarial loss weighting are shown", "weaknesses": "- Tables are missing an analysis of first frame latency and throughput (see Self Forcing). I suggest the authors to report these numbers. FFE will cause first frame latency to match the original 4 steps Self Forcing\n- The paper considers only the setting where chunks of 3 latent frames are predicted simultaneously and does not show results for frame-by-frame autoregressive generation. Frame-by-frame prediction is a setting of high practical importance as it minimizes latency. Evaluation would be strengthened by showing qualitatives and quantitative results for this setting too.\n- Evaluation is performed on the 5 second setting and no results are shown beyond it. Self Forcing can generalize beyond 5s generation, an important capability for an autoregressive causal generator. Such capability should be demonstrated and evaluated. Without this capability, practical significance of the method would be reduced.\n- The paper is unclear in some key parts as Algorithm 1 and discriminator design. See questions.\n- Some typos and missing spaces before citations", "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)