DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
Authors: Yingyan Li
Shuyao Shang
Weisong Liu
Bing Zhan
Haochen Wang
Yuqi Wang
Yuntao Chen
Xiaoman Wang
AnYasong
Chufeng Tang
Lu Hou
Lue Fan
Zhaoxiang Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: VLA
World model
End-to-end autonomous driving
Primary area: applications to robotics, autonomy, planning
TL;DR: DriveVLA-W0 uses world modeling to overcome the supervision deficit in VLA models, amplifying data scaling laws on large-scale driving datasets.

Abstract: Scaling Vision-Language-Action (VLA) models on large-scale data offers a promising path to achieving a more generalized driving intelligence.
However, VLA models are limited by a ``supervision deficit'': the vast model capacity is supervised by sparse, low-dimensional actions, leaving much of their representational power underutilized.
To remedy this, we propose DriveVLA-W0, a training paradigm that employs world modeling to predict future images.
This task generates a dense, self-supervised signal that compels the model to learn the underlying dynamics of the driving environment.
We showcase the paradigm's versatility by instantiating it for two dominant VLA archetypes: an autoregressive world model for VLAs that use discrete visual tokens, and a diffusion world model for those operating on continuous visual features.
Building on the rich representations learned from world modeling, we introduce a lightweight action expert to address the inference latency for real-time deployment.
Extensive experiments on the NAVSIM v1/v2 benchmark and a 680x larger in-house dataset demonstrate that DriveVLA-W0 significantly outperforms BEV and VLA baselines.
Crucially, it amplifies the data scaling law, showing that performance gains accelerate as the training dataset size increases.

Published as a conference paper at ICLR 2026
DRIVEVLA-WO: WORLD MODELS AMPLIFY DATA SCALING LAW IN AUTONOMOUS DRIVING
Yingyan Li
Shuyao Shang" Yuntao Chen
Weisong Liu'
Bing Zhan'
Haochen Wang
Yugi Wang!
Xiaoman Wang Yasong AnD
Chufeng Tang?
Lu Hou Lue Fan
Zhaoxiang Zhang
NLPR, Institute of Automation, Chinese Academy of Sciences (CASIA)
Yinwang Intelligent Technology Co. Ltd.
(Liyingyan2021, shangshuyac2024, Liuweisong2024, zhanbing2024)8ia. (lue. fan, zhaoxiang zhang) @ia.ac.c. ac
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5.50
Collsiin Rate (Nae)
5.25
Acrion Prediction)
Spanse Action Supervision
Visual &
5.00
4.75
TransFuser
Supervision
Decretse
VLA (Action Prediction)
DriveVLA-WO (World Modeling)
DriveVLA-WO
20
(World Modeling)
700K
7M
70M
Image
Image
Image
Number
Action Prediction vs. World Modeling
(b) Scaling with Data Size
Figure 1: World modeling as catalyst for VLA data scalability. (a): Unlike standard VLAs trained solely on action supervision, our DriveVLA-VV is trained to predict both future actions and visual scenes. (b): This world modeling task provides dense source of supervision, enabling our model to better harness the benefits of large-scale data.
ABSTRACT
Scaling Vision-Langussn Language- Action (VLA) models on large-scale data offers a promis- ing path to achieving a more generalized driving intelligence. However, VLA models are limited by "supervision deficit": the vast model capacity is supervised by sparse, now-dimensional actions, leaving much their representational power underutilized. To remedy this, propose DriveVLA-WE VLA-WO, training paradigm that employs world modeling to predict future images. This task generates a dense, self-supervised signal that compels the model learn the underlying dynamics of the driving environment. We showcase the paradigm's 's versatility by instantiating it for two dominant VLA archetypes: an autoregressive world model for VLAs that use discrete visual tokens, and a diffusion world model for those operating on continuous visual features. Building on the rich representations learned from world modeling, we introduce a lightweight action expert to address the inference latency for real-time deployment. Extensive experiments on the NAVSIM v1/v2 benchmark and a 680x larger in-house dataset demonstrate that DriveVLA- VLA-WO significantly outperforms BEV and VLA baselines. Crucially, amplifies the data scaling law, showing that performance gains accelerate the training dataset size increases.
INTRODUCTION
The promise of scaling laws (Kaplan al., 2020; Zhai et al., 2022; Baniodeh et al., 2025; Dehghani et al., 2023) presents an attractive path toward more generalized driving intelligence, with the hope that petabytes of driving data be harnessed train powerful foundation models. In the current

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Published as a conference paper at ICLR 2026
landscape, two dominant paradigms exist. On one side specialized models (Hu et al., 2022; Jiang et al., 2023) centered around Bird's-Ep Sedd's-Ee--ieee View (BEV) representations (Li et al., 2022; Huang et al., 2021). These models are built upon carefully designed geometric priors, which, while effective for driving-specific tasks, make it less straightforward to leverage non-driving datasets. In addition, their relatively compact architectures may constrain their potential for large-scale data scalability. In response, Laaga-agaangggttiiiiin 2025d) have emerged as a promising alternative. By leveraging large-scale Vision-Language Models (VLMs) (Wang et al., 2024b; Bai et al., 2025) pretrained on sccereet-scale data, VLAs possess a significantly larger model size and greater intrinsic potential for scaling.
(VLA) models (Fu et al., 2025a; Li et al., 2025b; Zhou et al.,
However, this scaling potential is largely unrealized due to critical challenge: the immense model size of VI.A models is met with extremely sparse supervisory signals. The standard paradigm involves fine-tuning these VLM models solely on expert actions. This tasks the model with mapping high-dimensional sensory inputs to a few low-dimensional control signals (e.g., waypoints). This creates severe "supervision deficif". This deficit prevents the model from learning rich world representations, a fundamental limitation that cannot be overcome by simply increasing the volume of action-only training data. In fact, we observe that without sufficient supervision, large VLA models can even underperform smaller, specialized BEV models.
To address this supervision deficit, harness the power of world modeling (Li et al., 2024a; Wang et al., 2025; Cen et al., 2025; Chen et al., 2024), integrating it as a dense seff-supervised objective to supplement the sparse action signal. By tasking the model with predicting future images, we generate a dense and rich supervisory signal at every timestep. This objective forces the model to learn the underlying dynamics of the environment and build rich, predictive world representation. To validate the effectiveness of our approach, we implement it across the two dominant VLA architectural families, which are primarily differentiated by their visual representation: discrete tokens versus continuous features. For VLAs that represent images as discrete visual tokens, world modeling is a natural extension. We propose an autoregressive world model to predict the sequence of discrete visual tokens of future images. For VLAs that operate on continuous features, this task is more challenging as they lack a visual vocabulary, making a direct next-token prediction approach infcasible. To bridge this gap, we introduce a diffusion world model that generates future image pixels conditioned on the vision and action features produced at the current frame.
We validate world modeling approach across multiple data scales, from academic benchmarks to a massive in-house dataset. First, experiments scaling at academic benchmarks reveal that world modeling is crucial for generalization, dataset-specific action patterns. To study true scaling laws, we then leverage a massive 70M-frame in-house dataset, as Figure 1 shows. This confirms our central hypothesis: world modeling amplifies the data scaling law. This advantage stems from the dense visual supervision provided by future frame prediction, creating a qualitative gap that cannot be closed by purely scaling the quantity of action-only data, Finally, to enable real-time deployment, we introduce a lightweight, MoE- based Action Expert. This expert decouples action generation from the large VLA backbone, reducing inference latency to just 63. % of the baseline VLA and creating an efficient testbed to study different action decoders at a massive scale. This reveals a compelling reversal of performance trends from smaller to larger data scales. While complex flow-matching decoders often hold an advantage on small datasets, we find that this relationship inverts at a massive scale, where the simpler autoregressive decoder emerges as the top performer. Our work makes three primary contributions:
it learns robust visual patterns rather than overfitting to
We identify the "supervision deficit" as a critical bottleneck for scaling VLAs and propose DriveVLA-We a paradigm that uses world modeling to provide a dense, self-supervised learning signal from visual prediction.
Our experiments reveal two key scaling advantages of world modeling. First, it enhances generalization across domains with differing action distributions by learning transferable visual representations. Second, on massive law, providing a benefit that simply scaling up action-only supervision cannot achieve.
dataset, it amplifies the data scaling
We introduce lightweight MoE-based Action Expert that reduces inference latency to 63.1% of the baseline. Using this expert as a testbed, we uncover a compelling scaling law reversal for action decoders: simpler autoregressive models surpass more complex fow-matching ones at a massive scale, inverting the performance trend seen on smaller datasets.
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RELATED WORK
VLAs in Autonomous Driving. The development of VLMs in autonomous driving has progressed through three main stages, evolving from focusing on interpretation to integrated end-to-end VLA architectures. The first stage, language-basee scene interpretation models, focused enhancing interpretability.. Models like DriveGPT4 (Xu et al., 2024) used Large Language Models (LLMs) to generate scene explanations and high-level maneuver suggestions, but without producing directly exe- cutable actions (Chen et al., 2025; Zhou et al., 2025c). The second stage, modular language-to-action frameworks, aimed to connect high-level commands with low-level control. These approaches (Zhou et al., 2025b; Yuan et al., 2024; Zhang et al., 2025; Arai et al., 2025; Gao et al., 2025) utilize multi- stage pipelines where modules are connected via non-differentiable interfaces, such as discrete textual commands, which prevent gradient back proppaaatnn The current frontier is End-to-Ein -ndEndd VLAs (Renz et al., 2025; 2024; Jiang et al., 2025; Hwang et al.. 2024; Yang et al., 2025; Zeng et al., 2025), which employ a unified architecture to directly map sensor inputs to trajectories. Within this paradigm, Orion (Fu et al., 2025a) enables holistic reasoning through vision-language instructed action gen- eration. Additionally, models like MindDrive (Fu et al., 2025b) and ReCogDrive (Li et al., 2025b) leverage reinforcement learning to effectively bridge the language- 2025d) tokenizes trajectories into action primitives, enabling single autoregressive model to learn adaptive reasoning and planning. We follow this paradigm and propose an end-to-end VLA.
gap. Auto (Zhou et al.,
World Models in Driving and Robotics Research world models typically follows two distinet philosophies: utilizing them as data synthesizers for simulation, or employing them as an auxiliary objective for representation learning. The first stream focuses on generation, aiming to synthesize high-quality driving data. Prominent works like GAIA-1 (Hu et al., 2023), DrivingGPT (Chen et al., 2024), and Doe-1 (Zheng et al., 2024a) generate multi-modal tokens for realistic scenarios. while unified models like Hermes (Zhou et al., 2025a) and UniFuture (Liang et al., 2025) simultancously perform future generation and 3D scene perception. Conversely, the second stream leverages world modeling as self-supervised objective to enhance representation learning. While VaVAM (Bartoc- cioni et al., 2025) demonstrates the value of generative video pretraining. it lacks action conditioning. Addressing this, UniVLA (Wang et al., 2025) and WorldVLA (Cen et al., 2025) incorporate actions into autoregressive prediction to learn robust policies. In autonomous driving, LAW (Li et al., 2024a) pioneers this paradigm using a latent world model. Distinct from LAW's ' latent prediction, our work supervises the model to predict future images, providing a richer and more direct dense learning signal for the VLA backbone.
3 METHODOL.OGY
Our methodology unfolds in three key steps. First, we establish a VLA Baseline to demonstrate the challenges of sparse action-only supervision. Second, we enhance this baseline with World Modeling, our primary contribution, which provides dense self-supervision. Building on this, we tackle the inference bottleneck by introducing a lightweight, MoE-based Action Expert, ensuring our powerful model achieves real-time performance.
3.1 VLA BASELINE
Our VisionLaagguaee-Nee front-view images (V1), and past actions (A,1)). To ensure broad applicability, we build variants on the two dominant VLM paradigms: VLA (VQ), which quantizes images into discrete visual tokens for an Etu3-style backbone, and VLA (VIT), which extracts continuous features for Qwen2.5- backbone.
(VLA) baseline processes sequences of language instructions (L.)
Input Tokenization. High-level driving language instructions (L₂) are processed using the VLM's native tokenizer. For past actions, we use the FAST tokenizer (Pertsch et al., 2025) to convert continuous waypoint trajectories into sequence of discrete tokens, denoted A
VLM Backbone. At each timestep f, we form a deeply interleaved input sequence S, by concatenating multimodal chunks over history of H steps following Wang et al. (2025); Fan et al. (2025): S, = [L, HI Vi-H,A,- A----.......... Le, V,, A,-1]. This sequence is processed autoregressively by our VIM backbone, for which select two representative models: Emu3 (8B) (Wang et al., 2024b)
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(a) AR World Model
(b) Diffusion World Model
Supervision
Supervision
Decader
VQ-GAN
Fneoder
VAE
Denoiser
Quantization
Add Noise
Noised
Gradient
FL
Quantized Vector
Image:
Imager+1
Latent
Latent
Noise
Condition
Next Token Prediction
F
FA
FV
PA
Fl11
F++1
F
Emu3 (8B) / Qwen2.5-VL (7B)
Text Tokenizer
Action * Tokenizer
Text * Tokenizer
Action c Tokenizer
VQGAN/VIT
VQGAN/VIT
"oo straight"
"turn left"
4
V
A
Le+1
Ve+1
A
Figure 2: The architecture of Drive VLA-WO WO, which achieves world modeling in two ways: (a) an AR World Model that predicts discrete visual tokens, and (b) Diffusson World Model that denoises latent representations conditioned on multimodal inputs.
to handle discrete visual representations and Qwen2.5-VL (7B) (Bai et al., 2025) for continuous features, using a causal attention mask. The VLM backbone outputs the final-layer hidden states, which are then split according to their respective modalities into language (F.),, vision (F.) ), and action (F(A) features.
Action Prediction. For training, optimize the model predict the ground-truth action token sequence A, = (A1,.... (01,...,aM)) using standard cross-entropy loss:
Lcction Σ log P(a aci).
During inference, the trained model then autoregressively generates a sequence of action tokens conditioned on the context St. These tokens are subsequently converted back into continuous waypoint trajectory by the FAST detokenizer (Pertsch et al., 2025).
3.2
WORLD MODELING
Prior VLA pipelines typically only supervise the model's actions. This yields a sparse supervisory signal, compressing nigh-dimensional sensory inputs into few oow-dimesiional control signals and results in "supervision deficit".. To address this, we introduce world modeling as a powerful self-supervised objective. We implement the world model differently
our two VLA paradigms. next-token For VLAs equipped with discrete visual vocabulary, we formulate the world model prediction task, creating our AR World Model. Conversely, for VLAs that operate on continuous visual features, we introduce Diffusion World Model generate future images in continuous latent space.
AR World Model. Our AR World Model predicts the current visual scene by autoregressively generating its sequence of discrete visual tokens, conditioned on past observations and actions (Figure (a)).
Training. The model learns to autoregressively generate the sequence of visual tokens for the current image, V,- (Vi,...; V,), conditioned on the preceding context Sev, The process is optimized by
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minimizing the next-token prediction loss
CWM-AR log P( P(u:|SeV,
(2)
We refer this complete framework DriveVLA-WO (VQ). It trained joindy by optimizing weighted sum of the action and AR world model losses: Looal = CAction + aLWM-AR. where a is balancing coefficient.
Inference. While the explicit generation of visual tokens is typically bypassed during inference to ensure low latency, this capability remains valuable for visualization purposes. To generate an image, the model autoregressively samples sequence of visual tokens, which are then passed to the MoVQGAN (Zheng et al., 2022) decoder to render the final image It.
Diffusion World Model. Unlike the VQ-based counterpart, our VIA (VTT) model lacks discrete visual vocabulary suitable for next-token prediction. We therefore introduce a Diffusion World Model, which instead provides dense supervision by training a latent diffusion model (Rombach et al., 2021; Wang et al., 2024a) to generate future images conditioned on the VLA's rich output features (FY, FFA) as Figure shows. This choice to predict the future frame (I++1) is critical: since the model is conditioned all present features simultaneously, predicting the future is necessary to learn predictive dynamics rather than simply performing a reconstruction task.
Training. This framework learns to predict the future visual scene (I++1) conditioned on the VLA's current visual and action features (FV and F.A). Following the standard latent diffusion setup, the model is trained denoise noised version of the future image's 's latent representation. This is optimized via an MSE objective
LWM-DIff B.... - E( FY,
(3)
where 2++1 is the latent of the future image I,+1, NO, is sampled Gaussian noise, k is random diffusion timestep, and é is the denoiser network trained to predict the noise from the noised latent 2 We term this overall framework DriveVLA-We (ViT). is trained end-to-end by optimizing a joint objective that combines the action prediction loss and the diffusion world model loss: Lrail L + DW where is a balancing coefficient.
Inference. As with the AR model, the diffusion process is bypassed during driving inference to ensure real-time performance. For qualitative analysis, future frames can be generated by running the reverse diffusion process, starting from random noise and conditioning on the features F.Y and F/A to produce predicted image 1441-
3.3 ACTION EXPERT
MoF. Architecture. (Black et al.; Intelligence et al., 2025) While our large VLA backbone excels at representation learning, its size is prohibitive for real-time control. To address this, we introduce lightweight action expert (500M) that operates alongside the main VLA Expert (our full VLA backbone) a Mixture-of-Experts (MoE) architecture. The Action Expert shares a similar transformer much smaller hidden dimension. This architectural
block structure with the VLA Expert but similarity enables a deep and efficient fusion of information via a Joint Attention mechanism as Figure 3() shows. In this setup, both experts first compute their respective Query, Key, and Value matrices. These matrices are then concatenated along the token sequence dimension to create single set of inputs for Joint Attention operation
Q [QVLA: QAEl,
K = (KLAA KAE], V [VVLA: VAul-
(4)
The resulting attention output is then split and routed back to each corresponding expert Figure shows. This approach allows for a tight, symmetric integration of the VLA's rich representations and the Action Expert's specialized context within single, efficient computation.
This efficient MoE architecture also serves as an ideal test bed for systematically investigating three autoregressive, and flow matching expert. distinct action decoding strategies: query-based,
key commonality among these variants is the prefilling of the previous action's 's features (A,-1). which provides strong temporal prior for the current decision.
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Ma)MoE Architecture
(b) Query-based
(c) Autoregressive
(d) Flow Matching
VLA Expert (8B)
Action Expert (500M)
Waypoint
Waypoint
Waypoint
LayerNorn & FFN
LayerNorm & FFN
MLP
Detokembarr
Denormatize
+
Denoised Action
Joint Attention
Updated Query!
Action Toksn
T
LayerNonn
LayerNorm
Action Expert
Action Expert.
Action Expert
XW
Prefill
Prefill
Prefill
Prefill
ded
Denoise
Ac-2 Ve Ac-1
Ap-1
Waypoint Query
Ac-1
Query
Ac-1
Token
Noised Action
Figure 3: (a) Our Mixture-of- -oxpertss (MoE) architecture pairs large VLA Expert with lightweight Action Expert for efficient inference. (b-d) This framework serves as a testbed for comparing three action decoding schemes: query-baser autoregressive, and flow matching.
Query- Action Expert. This expert employs set of learnable action queries that interact with the VLA's multimodal context via joint attention. The resulting updated queries are then projected by an MLP head to directly regress the continuous waypoint trajectory. The model optimized by minimizing the LI distance between the predicted and ground-truth trajectories.
Autoregressive Action Expert. This expert generates actions by autoregressively predicting sequence of discrete tokens. Its training objective and formulation are identical to those used for our VLA Baseline (described in Section 3.1), minimizing a standard cross-entropy loss.
Flow Matching Action Expert. In contrast to the discrete nature of the autoregressive approach, we also implement a continuous action generation method based on flow matching. This method learns conditional vector field, Ug. that defines direct "path" from a simple noise distribution to the complex distribution of real-world driving actions. During training, we define a simple straight- line trajectory between random noise sample and ground-trute action (Liu et al., 2022). The model is then optimized via a mean squared error loss to predict a vector field Up that aligns with this trajectory at each point, conditioned the multimodal context C₁. For inference, noise sample and follow the learned vector field for a fixed number of steps using a numerical ODE solver. This process deterministically transforms the noise into precise, continuous action that lies on the learned data manifold.
simply start with
EXPERIMENT
4.1 DATASETS
NAVSIM. We use the NAVSIM (Dauner et al., 2024) benchmark derived from OpenScene (Contribu- tors, 2023), for evaluating performance in safety-critical scenarios.
NAVSIM v/ metrics include No at-fault Collision (NC), Drivable Area Compliance (DAC), Time-To To-
Collision (TTC), Comfort (C.), and Ego Progress (EP). NAVSIM uses the Predictive Driver Model Score (PDMS) to evaluate model performance: PDMS = NC x DAC' x DxEP+5xTTC+22
12
NAVSIM v2 (Cao et al., 2025) includes several components. categorized as penalties or weighted subscores. Key metrics are No at-fault Collision (NC), Drivable Area Compliance (DAC), Driving Direction Compliance (DDC), Traffic Light Compliance (TLC), Ego Progress (EP), Time to Collision (TTC), Lane Keeping (LK), History Comfort (HC), and Extended Comfort (EC). NAVSIM v2 uses the Extended Predictive Driver Model Score (EPDMS) evaluate model performance: EPDMS
NC x DAC x DDC x TLC x
In-house Dataset. To test data scalability beyond academic benchmarks, we use a massive in-house dataset for training and evaluation. Our training set contains 70 million frames from over / million unique clips. is curated to be diverse and balanced across a wide spectrum of driving scenarios, while being significantly enriched with challenging and safety-critical events. The test set comprises 100 challenging scenarios. We evaluate trajectory using the Average Displacement Error (ADE) over 3-second, 6-waypoint future trajectory (2 Hz), and safety using Collision Rate. We compute our Collision Rate using the same methodology as the No at-fault Collision (NC) metric from the NAVSIM benchmark.

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Table Comparison with state-of-the-art methods on the NAVSIM v1. NC: no at-fault collision. DAC: drivable area compliance. TTC: time-to-collision. C.: comfort. EP: ego progress. PDMS: the predictive driver model score. Abbreviations: Ix Cam (single front-view camera), Nx Cam (surround- view cameras), L (LiDAR). ": Using the query-based action expert. f: Using the query-based action expert with multiple trajectory anchors following Li et al. (2024b). $: Using the AR action expert with the best-of-N (N=6) strategy following Zhou et al. (2025d).
Method
Ref
Sensors
NC
DAC
TTC1
C.
EP
PDMS
Human
100
100
100
99.9
87.5
94.8
BEV-bosed Methods
UniAD (llu el al., 2022)
CVPR 23
6x Cam
97.8
91.9
92.9
100.0
78.8
83.4
TransFuser (Prakash et al., 2021)
TP
3x Cam L
97.7
92.8
92.8
100.0
79.2
84.0
PARA-Drive (Weng
2024)
CVPR'24
Cam
97.9
92.4
93.0
99.8
79.3
84.0
LAW 2024a)
ICLR'25
Ix Cam
96.4
95.4
88.7
99.9
81.7
84.6
Hydra-MDD (Li ct al., 2024b)
arXiv'24
3x Cam L
98.3
96.0
94.6
100.0
78.7
86.5
DiffusionDrive (Lian et al., 2025)
CVPR'25
3x Cam I.
98.2
96.2
94.7
100.0
82.2
88.1
WoTE (Li al., 2025a)
ICCV'25
3x Camn L
98.5
96.8
94.4
99.9
81.9
88.3
VLA-based Methods
DriveVLA -L-WO-
1x Cam
98.7
96.2
95.5
100.0
82.2
88.4
ALA (Zhou al., 2025d)
NeurlPS'25
Cam
98.4
95.6
99.9
ReCogDrive (Li et 2025b)
arXiv'25
3x Cam
98.2
97.8
95.2
99.8
83.5
89.6
DriveVLA-Wof
Ix Cam
98.7
99.1
95.3
99.3
83.3
90.2
AutoVLAf (Zhou ot al., 2025d)
25
3x Cam
99.1
97.1
97.1
100.0
87.6
92.1
DriveVI.A- -W0
1x Cam
99.3
97.4
97.0
99.9
88.3
93.0
Table 2: Comparison with state-of-the--rt methods the NAVSIM v2 with extended metrics.
NC: No at-fault Collision. DAC: Drivable Area Compliance. DDC: Driving Direction Compliance. Traffic Light Compliance. EP: Ego Progress. TTC: Time Collision. LK: Lane Keeping, HC:
TLC:
History Comfort. EC: Extended Comfort. EPDMS: Extended Predictive Driver Model Score.
Method
NC
DAC
DDC1
TLC f
EP
TTC
LK
HC
EC
EPDMS
Ego Status
93.1
77.9
92.7
99.6
86.0
91.5
89.4
98.3
85.4
64.0)
TransFuser (Prakush 2021)
96.9
89.9
99.7
87.1
87.2
76.7
HydraMDP++ (1.i al., 2024h)
97.2
97.5
99.4
99.6
83.1
96.5
94.4
98.1
70.9
81.4
DriveSuprem (Yao et al., 2025)
97.5
96.5
99.4
99.6
88.4
96.6
95.5
98.3
77.0
83.
ARTEMIS (Feng 2025)
98.3
95.1
83.1
Drive (Lino et al,, 2025)
98.2
95.9
99.4
99.8
87.5
97.3
96.8
98.3
87.7
84.5
DriveVLA-WO
98.5
99.1
98.0
99.7
86.4
98.1
93.2
97.9
58.9
86.1
4.2 IMPLEMENTATION DETAILS
Two-stage Training Paradigm. Our model trained via a two-stage paradigm designed to first learn rich world representations and then specialize in action generation. In the first stage, pretrain the VLA backbone utilizing the 6VA sequence configuration. The model is optimized with joint objective, combining both the world model loss and the action prediction loss. the second stage, we integrate the model with Action Expert. The VLA backbone now processes 2VA input sequence. While we do freeze the VLA backbone, the model only supervised by the action loss from the action expert part.
NAVSIM. For experiments on the NAVSIM benchmark, models are pretrained on NuPlan (Caesar et al., 2021) for 8k steps and then fine-tuned on NAVSIM for 4k steps, processing 256x 144 images. The training is conducted on NVIDIA 1.20 GPUs with global batch size of 48. We used the AdamW optimizer with cosine learning rate schedule, initial learning rate of 2e 4, and 6 mixed- precissoe. For our ablation studies, we select DriveVLA-WO (VQ) as the default model due to its architectural simplicity.
In-house Dataset. For our large-scale experiments on the in-house dataset, models are pretrained for 50k steps and fine-tuned for 30k steps using the same data. This training utilized cluster of 64 GPUs with a global batch size of 256. The optimizer and learning rate schedule remained identical to the NAVSIM setup.
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World model enhances generalization to
action distributions
204-
95
NuPlan Pretrain NAVSIM Finetune
World Model Visuat
POMS (Higheris Better)
90
NAVSIM (Train from Scratch)
Only Action
Lenpaded troppemin
NuPan
NAVSM
80
100%
75
70
65
60
TransFuser-7B VLA (VQ) (VA VLA-WO (VQ) VLA-WO (VIT]
Different Action Distribution
[b) Similar Visual Distribution
Figure 4: World modeling unlocks generalization with data scaling, Our world model turns pretraining from detriment for pparse-supervisiig baselines (red arrows) into benefit for VLA-WOs (green arrows), enabling positive knowledge transfer across datasets with similar visuals (b) but different action distributions (a). Figure (a) is from Dauner al. (2024).
Re-implemented TransFuser. To provide a solid baseline for our in-house dataset, we adapt and re-implement the well-known TransFuser (Prakash et al., 2021) architecture. For fair comparison with our single-camera setup, modify Latent-TransFuser (an image-only variant of TransFuser) to process single front-vies view image instead of its original multi miwt input. To investigate the impact 50M-parameter
of model size, we implement versions: a 50M parameter model and a 7B 7B-parameter model. The smaller TransFuser-S0M uses standard ResNet-34 backbone. The larger TransFuser uuser-BB employs a VTT-7B backbone, initialized with pretrained weights from DINOv3 (Siméoni et al., 2025).
4.3 COMPARISON WITH
METHODS
As presented Table and Table 2, DLive W0 establishes a new state-of-the-ard on the NAVSIM benchmark by surpassing top-performing methods across different architectural paradigms, including the BEV-based WoTE (Li et al., 2025a) and the VLA-based AutoVLA (Zhou et al., 2025d). Notably, our model achieves this top performance using only a single front-view camera, surpassing com- petitors that rely on richer sensor suites combining multi-view cameras and LiDAR. This superior performance is attributed to the powerful, dense supervision from our world modeling objective, which enables the model to learn more effective feature representation.
4.4 WORLD MODELS AMPLIFY DATA SCALING LAW
World modeling unlocks generalization with data scaling. To test generalization across differing action distributions, we evaluate models by pretraining on the large-scale NuPlan dataset and then fine-tuning NAVSIM, smaller benchmark focused challenging, long-tail maneuvers. This setup creates a significant domain shift in the action space while the visual domain remains similar, posing a challenge for knowledge transfer. As shown in Figure 4 and Table 7, world modeling is crucial for effective transfer. We observe two opposing trends: 1) Baseline models that rely solely on sparse action supervision often suffer from pretraining. For instance, TransFuser-7B and our VLA (VQ) baseline show significant performance degradation (red arrows). This is due to overfitting to NuPlan's 's action distribution, which creates a detrimental prior that hinders adaptation to NAVSIM's corner cases. 2) Our VLA-WO models consistently benefit from pretraining. By forcing the model to predict future visual frames, the world modeling encourages the learning transferable visual representations of the environment. These transferable visual representations lead to superior generalization.
World modeling outperforms action-only supervision with data scaling. We investigate data scalability by training models on three data scales (70k, 700k, and 70M frames). To cleanly ablate the impact of world modeling paradigm, models, excluding the action experts. As shown Table 3, the world model critical for unlocking the benefits of large-scale data. While baseline models that rely sparse action supervision quickly
conduct these experiments directly the base VLA
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show performance saturation, DriveVLA-We WO models demonstrate sustained improvement. The impact is most pronounced at the 70M-frame scale. Here, adding world modeling substantially boosts performance, improving the VQ model's ADE by 28.8% and the ViT model's collision rate by 15.9%. This demonstrates that even with a massive volume of training data, action-only supervision cannot replicate the qualitative advantage provided by dense world model objective.
Table World modeling outperforms action-only supervision with data scaling. Unlike baseline models that plateau early under sparse supervision, our VLA-WO models show consistent improve- ment.
In-house Dataset Scale (Number of Frames)
Model
70k
700k
70M
ADE (m) 4
Collision (%)
ADE (m)
Collision (%)
ADE (m)
Collision (%)
TransFuser-50X.
2.5893
0.0891
1.7461
0.0563
1.2627
0.0:72
TransFuser-77:
2.5757
0.0839
2.1391
0.0710
1.2244
0.0539
VLA (VQ) (Baseline)
2.8520
0.0982
1.5424
0.0565
1.4829
0.0488
+
World Model (Ours)
2.7482 13.6%
0.0956
12.7%
1,5985
13.6%
0.0520 18.0%
1,0563
128.8%
0.0392 119.7%
VLA (VIT) (Baseline)
3.1524
0,0950
1.4202
0.0:12
1.1051
0.0359
World Model (Ourx)
2.5268 119.9%
0.0834 112.2%
1.3436 (5.4%
0,0513 1.00%
1,0640 13.7% 0,0302 15.99%
Action experts reverse performance with data scaling. Our comparison of action expert on NAVSIM (100k frames) and in- in-house dataset (70M frames) reveals a striking performance reversal, driven by trade-off between prediction precision and modeling capacity. As shown in Table 4, on the small-scale NAVSIM dataset, continuous decoders like query-based and flow matching excel. Here, the trajectory distribution is simple. The higher precision gives these experts edge over the discrete autoregressive approach, which hindered by quantization error. However, on our massive dataset, modeling vastly more complex trajectory distribution becomes the dominant challenge. In this high-data regime, the autoregressive decoder's 's strong modeling capacity and sample-efficient, teacher-forced training allow scale most effectively. Conversely, the query- based expert faces a representational bottleneck, and the flow matching expert proves too cample-inefficien to converge on the complex data manifold, leading to the observed performance reversal.
Table 4: Action experts reverse performance with data scaling, For each dataset, all three experts initialized from an identical pretrained VLA model. Notably, the query-based expert that performs best on the small-scale data is surpassed by the autoregressive expert at the larger scale, highlighting clear performance reversal.
NAVSIM (103k Frames)
In-house Datasel (70M Frames)
Action Expert
NC f
DAC 1
T
C.
EP
PDMS
ADE (m) $
Collision (%)
Query-based
98.7
96.2
95.5
100.0
82.2
88.4
1.1248
0.0453
Flow Matching
98.4
95.3
95.2
100.0
80.9
87.2 (1.4%
1.0362 17.9%
0.0398 12. 1%
Autoregressive
98.4
93.6
94.5
100.0
79.3
85.3 13.6%
1.0069 110.5%
0.0295 134.9%
4.5
ABLATION STUDY
The following two world model ablations are conducted without the action experts.
Vision-Only vs. Vision-Action Sequence As shown in Table 5, pretraining with an interleaved vision-action sequence (6VA) yields substantial gains over a baseline pretrained with only a vision loss (6V), improving the PDMS score from 84.1 I to 85.6. This demonstrates that grounding visual predictions corresponding ego actions is crucial. This conditioning compels the model learn the environment's 's underlying causal dynamics, it must predict the specific visual outcome of action rather than a generic or ambiguous future.
Ablation Sequence Length We ablate the pretraining sequence length using three configurations: VA, 2VA, and 6VA. Table reveals a clear trend where performance scales with temporal context, with the longest sequence (6VA) achieving the best results. This highlights that longer context window is critical for learning complex, long-horizer environmental dynamics, as it enables the model to better capture temporal dependencies for planning.

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Published as a conference paper at ICLR 2026
Table Ablation study vision-only vs.
Table Ablation study on varying the se- quence length.
vision-action sequence design.
Pretrain (NuPlan)
Finetune (NAVSIM)
Pretrain (NuPlan)
Finetune (NAVSIM)
NC
D
PDMS T
NC
DAC1
PDMS
2VA
97.1
90.3
80.7
VA
VA
96.8
92.7
83.3
2VA
97.9
92.8
84.1
2VA
2VA
97.3
93.2
84.2
2VA
98.3
93.8
85.6
6VA
2VA
98.3
93.8
85.6
Ablation Latency We validate the efficiency of MoE architecture by measuring inference lateney on an H200 GPU. Compared to the baseline DriveVLA-Wi (117.8ms latency, 85.6 PDMS), the addition of query-based MoE expert significantly cuts the latency 74.3ms (just 63.1 of the original) while simultaneously boosting performance to 88.4 PDMS. More analysis is shown in AppendixB.1.
4.6 VISUALIZATION
Beyond achieving superior planning performance, PriveV exhibits strong capabilities in action-conditioned future generation, highlighting its potential as reactive simulator. As illustrated in Figure 5, explicitly condition the model a counterfactual "decelerate" trajectory. The resulting frames show the surrounding scene flowing past the vehicle at a noticeably reduced rate compared to the ground truth. This precise alignment between control signals and visual dynamics confirms the model's grounded physical understanding. For further qualitative analysis, including trajectory visualization and case comparisons (Figures 7, 8, 9, 10), future image generation (Figure 13), and counterfactual reasoning (Figure 14), please refer to Appendix C.
V11
Input
Ground
Gl Tmj.: .0.00
Pred. Tnj.:
Comd. GT Traj
Cond.
Stop Action
Figure 5: Action-Conditioned Simulation. demonstrate the utility of our world model as simulator capable of generating diverse futures based control signals.
CONCLUSION
In this work, we identified the "supervision deficit" as fundamental bottleneck hindering the models in autonomous driving. We proposed DriveVIAA-W paradigm that remedies this issue by employing future image prediction as dense, self-supervised objective, tailored for both VQ- and ViT-based architectures. Our extensive experiments demonstrate that this approach not only unlocks superior data scalability and generalization compared to baselines, but also reveals a compelling performance reversal among action decoders at scale, where simpler autoregressive models ultimately prevail. Ultimately, findings suggest that embracing dense, predictive world modeling is crucial step toward realizing the full potential large-scale data in the pursuit of more generalized driving intelligence.
scalability of Vision- Lirion-LangaageAetttin
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6 ACKNOWLEDGMENTS
This work was supported in part by Beijing Natural Science Foundation (No. L257015, No. L257004) and in part by the National Natural Science Foundation of China (No. 62320106010).
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APPENDIX
A Overall
15
More Experiments
15
B.1 Latency Analysis
15
B.2 Positive Correlation Between Generative Fidelity and Planning Performance
16
B.3 Ablation Study
17
C
Visualization
17
C.1 Planning Trajectory
17
C.2 Failure Case
18
C33 Future Image Generation
19
C,4 Counterfactual Reasoning
21
More Implementation Details
More Related Work
22
F Use of LLMs
22
A OVERALL
In this appendix, provide supplementary materials to support the main paper. $B presents additional experiments, including detailed latency analysis, a study on the correlation between generative fidelity and planning performance, and ablation on the world model's time horizon. We offer further qualitative results in &C, with visualizations of challenging case studies and images generated by our world model. More implementation details for our baselines and training setup are provided in $D. An expanded discussion of related work is available in ŞE. We disclose our use of LLMs for writing assistance in $F.
B MORE EXPERIMENTS
Table 7: World model enhances generalization to new action distributions. This table presents the detailed result of Figure 4.
NAVSIM (Train from Scratch)
NuPlan Pretrain NAVSIM Finetune
Model
NC
DAC
C.
EPT
NC1
DAC
TTC
f
EP
PDMS
TransFuser-Sin
91.7
86.2
100.0
96.0
90.2
13.5%
TransPuser-7B
95.7
89.1
89.5
100.0
73.0
77.9
93.7
84.2
86.3
100.0
67.0
71.6
18.1%
VLA-VO VQ
93.3
80.9
86.0
100.0
63.0
68.7
90.0
76.1
82.2
100.0
56.7
62,2
19.5%
VLA-WD TLA-WO-VQ (Ours)
97.1
90.3
92.3
100.0
74.9
80.7
98.3
93.8
94.2
100.0
80.0
85.6
16.1%
VLA VIT
93.5
82.5
86.8
100.0
64.6
70.3
93.3
82.8
86.9
100.0
64.7
70.6
10.4%
VLA-WD A-- (Ours)
98.0
92.6
94.2
100.0
77.7
83.9
98.3
93.5
94.8
100.0
79.1
85.3
11.%%
B. LATENCY ANALYSIS
We analyze the inference latency of our MoE- based action experts against the full DLivingVLA- WO backbone, with results shown in Figure 6. In setup, the flow matching expert is configured to
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DrivingVLA-WO (w/o MoE)
300
DrivingVLA-WO + AR Expert
DrivingVLA-WO Flow Matching Expert
DrivingVLA-WO + Query-based Expert
250
Latency (ms)
200
150
100
50
5
10
15
20
25
30
(number of tokens)
Figure 6: Latency analysis. The lateney of the AR expert and the VLA baseline scale linearly with the number of generated tokens L, while the flow matching and query query-baset -based experts maintain constant inference time.
use 10 denoising steps, while the query-basee expert generates the full trajectory a single forward pass. The latency of the AR expert, similar to the full VLM backbone, is proportional to the number of generated action tokens L. The results clearly demonstrate that the MoE. architecture provides substantial acceleration. The query-basee expert is the most efficient, maintaining constant low latency of approximately 74ms. The flow matching expert also has a constant latency, albeit higher at around 145ms. To contextualize the AR expert's performance, token length. On the NAVSIM dataset, where trajectories average 5.6 tokens, the AR expert is highly efficient at 95ms, significantly outperforming the full backbone (1 18ms). For the more complex trajectories in our in-house dataset, which average 17.8 tokens, the AR expert's 's latency increases to 170ms, but remains much faster than the baseline's 240ms. This analysis confirms that our MoE framework is critical for achieving the low-latency performance required for real-time deployment.
consider the average trajectory
B.2 POSITIVE CORRELATION BETWEEN GENERATIVE FIDELITY AND PLANNING PERFORMANCE
To investigate the link between world model's generative fidelity and its downstream planning performance, we conduct this experiment. First, establish two models with different genera- tive capabilities by pretraining them on NuPlan with varying context lengths: 6VA model con- ditioned on five prior vision-action pairs, and 2VA model conditioned on just one. As pected, evaluating these checkpoints directly shows that the 6VA model's longer context yields superior FID score (Table 8), confirming its higher generative fidelity. With these mod- els of varying generative quality established, we then test their potential for downstream planning. We fine-tune both NuPlan- -uraan-pretrained checkpoints on NAVSIM under identical setting (the same the bottom two rows in Table 5) and evaluate their final PDMS scores. The results confirm a strong positive correlation: the 6VA checkpoint, which had superior generative fidelity, also achieves higher planning performance after fine-tuning, This provides compelling evidence that the model's 's ability to generate high-quality, realistic future images is directly linked to its capacity for producing high-quality trajectories.
Table 8: Positive Correlation Between Genera- tive Fidelity and Planning Performance. *: indi- cates the images reconstructed by the MoVQGAN seooder-decodee which serves as an upper bound for generative quality.
Sequence Design
FID 1
PDMS
2VA
9.847
84.1
6VA
4.610
85.6
Upper Bound"
3.007
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Published as a conference paper at ICLR 2026
B.3 ABLATION STUDY
The Time Horizon of World Model We conduct an ablation study to identify the optimal temporal horizon for our world model's input. Using shared 6VA-pretrained checkpoint, fine-tune and evaluate three configurations that differ in their temporal input range, as shown in Table 9: i) VA: Uses only the current visual frame, providing no historical visual context, ii) VAVA (1s): Uses the current frame and second frame from second in the past. ii) VAVA (4s): Uses the current frame and second frame from seconds in the past. The VAVA (1s) configuration achieves the best overall performance, reaching the highest PDMS score of 85.6. This suggests optimal trade-off the temporal input. The VA setting, lacking a second visual frame, struggles to capture the environment's dynamic information. Conversely, the 4-second interval in the VAVA (4s) setting introduces excessive scene variation between the two distant frames, which likely makes the future prediction task more challenging for the model.
C VISUALIZATION
C.I PLANNING TRAJECTORY
In this section, we provide qualitative case analysis to offer deeper insights into our model's hehavior and validate our key findings. Our analysis is twofold. First, shown in Figure DriveVLA VLA-WO against baseline models (VLA baseline and TransFuser) in a challenging corner case visually demonstrate the benefits of world modeling for navigating complex scenarios. Second, we compare the trajectories generated by different action experts built upon the same VLA backbone. The subsequent visualizations Figure 8,9,10 reveal that the AR expert generates markedly more stable trajectories than the fow-matching approach, particularly in terms of inter-frame consistency and avoiding aggressive maneuvers.
compare our
Front-view Image
TransFuser
VI.A Raseline
DriveVT.A-WO
NPC
Ego
Traj.
Traj.
Figure 7: World modeling improves trajectory planning in complex scenarios. TransFuser and VLA baseline often fail such interaction scenarios due to their weak ability to predict scene dynamics. However, with the support of world modeling, VLA-WO possesses strong predictive capabilitics, thereby avoiding collision in this type scenario.
Table 9: Ablation on the temporal interval for world model inputs. The results reveal clear performance trade-off, with I-second interval being optimal. Using only the current frame (VA) lacks sufficient temporal context, while 4-second interval introduces excessive scene variation.
Pretrain Type
Finetune Type
TI
NC
DAC
TTC t
Comf. 1
EP 1
PDMS +
6VA
VA
96.6
92.6
91.2
100.0
78.8
82.9
6VA
2VA
97.9
93.2
93,9
100.0
78.3
84.3
6VA
2VA
1s
98.3
93.8
94.2
100.0
80.0
85.6
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Published as a conference paper at ICLR 2026
Front-view Image
Flow Matching
Autoregressive
T
Unstab
Stable
T+1s
NPC
Ego Car
Lane
GT Traj.
Pred Traj.
Figure Comparison flow matching and autoregressive action expert. The trajectory generated by the flow matching action expert is relatively unstable, with jumps occurring between adjacent frames, while the AR action expert generates much more stable trajectories.
Front-view Image
Flow Matching
Aubreggressiee
Unstable
Stable
T+1s
NPC
Ego Car
Lane
GT Traj.
Pred Traj.
Figure Comparison of flow matching and autoregressive action expert. The trajectory generated by the flow matching action expert is relatively unstable, with jumps occurring between adjacent frames, while the AR action expert generates much stable trajectories.
C.2 FAILURE CASE
To provide a balanced view of our model's performance and identify directions for future improvement, analyze representative failure cases encountered during evaluation. We categorize these failures into two primary types: instruction ambiguity and dynamic prediction errors. As illustrated in Figure I1, coarse-grained navigation commands can lead to indecision in complex road topologies
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Front-ve view Image
Flow Matching
Autoregressive
Unstable
Stable
NPC
Ego Car
Lane
GT Traj.
Pred Traj.
Comparison of flow matching and autoregressive action expert. The trajectory generated by the flow matching action expert is relatively unstable, and it may go beyond the drivable area. The AR action expert generates much more stable trajectories.
Figure 10:
(e.g., Y-junctions). Additionally, Figure 12 highlights the challenge of predicting fine-grained dynamic objects (c.g., oncoming vehicles) in high-interae iighinteracction scenarios.
Failure Case Analysis
Input
Ground Truth
GT Traj.: 0.0.0
Pred. Traj: 0.000
Cond. on GT Traj
Cond. on Prod.Traj
V
Ves
Figure 1I: Failure case analysis: ambiguity in driving commands. illustrate a failure mode stemming from the ambiguity of coarse graiined navigation instructions. In this Y-shaped intersection. the model receives a generic "go straight" command. Due to the splitting road topology, this instruction is ambiguous,
maps clearly neither the left nor the right branch. Consequently, the model fails to commit to a valid lane, hesitantly driving straight into the fork area (as shown by the predicted red trajectory). This highlights limitation in the current NAVSIM benchmark, where the discrete command set (restricted to simple Turn Left/Straigh//ighh lacks the granularity to resolve ambiguities in complex geometries. Note: To faithfully represent the visual information available to the model, all displayed images are reconstructions from the MoVQGAN tokens, rather than raw RGB frames.
C.3 FUTURE IMAGE GENERATION
Our world model demonstrates both high visual fidelity and strong action consistency. As shown in Figure 13, it generates realistic and plausible images across diverse and challenging scenarios. More importantly, these visual predictions tightly coupled with the model's planning process.
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V
Failure Case Analysis
Input
Ground Truth
GT Traj:::: 000.0 Pred. Tmj. 00000
Cond. on GT Traj
Cond. on Prad.Traj
Figure 12: Failure case analysis: dynamic object prediction. While our world model generalizes well most dynamic scenes, it faces challenges in predicting multiple objects at complex intersections. In this specific case, the world model fails to anticipate the emergence of oncoming vehicles in the future frames (as reflected the Cond. GT Traj). Consequently, the planner, unaware of the potential conflict, incorrectly executes a left turn. This highlights the modeling of fine-grained dynamic objects valuable direction for future research. Note: To faithfully represent the visual information available to the model, all displayed images are reconstructions from the MoVQGAN tokens, rather than raw RGB frames.
V,s
Via
V..
V.
Inpur:
Pred.
(a) Complex intersections
Input!
GT
Pred.
(b) Static traffic jam scenarios
Figure 13: Future image generation. Our world model demonstrates strong generative fidelity, pro- ducing visually realistic and contextually plausible futures across diverse and challenging scenarios, including complex intersections and dense traffic.
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C.4 COUNTERFACTUAL REASONING
This section investigates the model's 's capability for counterfactual reasoning, synthesizing alternative futures based on hypothetical actions. shown in Figure 14, conditioning the model on "turn right" action generates realistic off-road imagery despite the ground truth being straight. This confirms that the model captures the underlying scene geometry rather than merely memorizing training trajectories.
V
Counterfactual reasoning
Input
Ground Truth
Cond. Turn Right Action
ViI
V
r12
V
V 14
Vaa
V
Counterfactual reasoning
Input
Ground Truth
Cond. on Tum Right Action
V
Ves
H3
Figure 14: Counterfactual reasoning. By conditioning counterfactual "turn right" action the roadside, our world model generates realistic video sequences where the vehicle deviates from the ground truth trajectory and drives off-road. This validates that the model has learned the underlying scene geometry rather than simply memorizing the training data.
D MORE IMPLEMENTATION DETAILS
TransFuser For consistency of the setting, we adopt Latent TransFuser, which replaces the LiDAR branch's 's real sensor features with BEV latent queries. The camera branch supplies the primary semantic and appearance features, while the latent BEV branch provides learnable global query anchors; the two are aligned and made complementary via cross-modal voossattenttion In this way, Latent TransFuser retains the global geometric priors the BEV view and achieves stable fusion and planning performance under a vision-only setup. We instantiate two image backbones highlight capacity trade-offs: a lightweight ResNer-34 variant (TransFuser-SOMA) and large DINOv3 ViT-7B Concretely, the inputs BEV query tensor Qiev of size H x W. After an image encoder (ResNet-34 or DINOv3) and BEV latent encoder, obtain F Fing and Frec, which are then aligned and fused through multi-scale
variant (TransFuser-27
single front-view RGB image I and latent
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cross-attention module to produce the fused representation Fuse- A planning head predicts K future waypoints fPi+1....,, ,P+K)) } based on F fuse-
E MORE RELATED WORK
Scaling Laws in Deep Learning. Kaplan et al. (2020) were the first to systematically demonstrate that pretraining loss scales a power law with model size, dataset size, and computational cost. Chinchilla (Hoffmann et al., 2022) then showed many LMs were undertrained and derived compute- optimal prescription that scales model size and tokens proportionally. In computer vision, Zhai et al. (2022) charted ViT scaling law with stable training recipes, and ViT-22B (Dehghani et al., 2023) scaled ViTs 22B parameters, verifying predictable multi-task improvements. Lin al. (2025) conducted a large-scale study of imitation-learning data scaling in robotics, and found near power-law gains from increasing environmental and object diversity with improved zero-shot generalization, In autonomous driving. STR (Sun et al., 2023) shows large trajectory models scale steadily in both prediction and planning, and Baniodeh motion forecasting and planning with large driving datasets. For end-to-end driving, Naumann et al. (2025) observe roughly log-linear gains both open- and closed-loop metries as training data scale increases. Zheng et al. (2024b) also observe power-law improvements from data scaling in a large-scale imitation learning study. However, Zheng et al. (2024b) exclusively analyzes scaling behavior within the conventional paradigm of sparse action supervision. Our work, in contrast, provides the first study of how these scaling laws are reshaped by the self-supervised signals provided by world modeling.
(2025) reported power-law improvements for joint
F
USE OF LLMS
Large Language Models (LLMs) employed to polish the writing in this manuscript.
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Reviews:
[{"review_id": "WcNEeKqLRx", "reviewer": "Reviewer_kyet", "summary": "This paper presents DriveVLA-W0, a Vision-Language-Action (VLA) model augmented with a world model to predict future visual frames. The goal is to provide denser self-supervised signals to compensate for the sparsity of action labels in end-to-end driving. Two variants are proposed: an autoregressive world model for discrete (VQ-tokenized) features and a diffusion-based world model for continuous visual features. The paper claims that the proposed method amplifies data-scaling behavior—i.e., performance continues to improve as data grows. Results on NAVSIM and a large-scale in-house dataset show consistent improvements in driving performance and sample efficiency.", "questions": "* Can the learned world model predict future observations conditioned on different actions, and could it be leveraged for simulation or counterfactual reasoning?\n* How sensitive is the performance to the weighting between the action-prediction and world-modeling losses?\n* Could the authors include more qualitative visualizations or failure cases to better illustrate model behavior and limitations?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The empirical analysis of data scaling is thorough and carefully conducted across multiple model and data sizes.\n* Both autoregressive and diffusion-based variants are implemented, illustrating generality across different architectures.\n* The paper contributes valuable large-scale empirical findings to the community, especially around scaling trends and model efficiency.", "weaknesses": "* The core contribution is incremental. The main idea—adding a future-frame prediction objective as an auxiliary loss—is conceptually simple and has been explored in prior work on world models (e.g., WorldVLA, UniVLA).\n* The proposed “world model” is used only as auxiliary supervision during training and not for downstream planning, simulation, or closed-loop control, limiting its conceptual impact.\n* Although the paper introduces a “Mixture-of-Experts” Action Expert, the 8B VLA backbone is still required at inference. The MoE design primarily reduces the cost of the action decoding stage rather than removing the need for the large backbone itself, so the memory footprint and deployment complexity might remain dominated by the 8B model.\n* Qualitative visualizations of predicted future frames with different actions are missing.", "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": "Pirj6z7Wmf", "reviewer": "Reviewer_83sR", "summary": "This paper introduces DriveVLA-W0, a method that utilizes future image prediction as an auxiliary training objective in addition to the action prediction objective. The paper investigates this method on both VQ- and ViT-based architectures. Experimental results on NAVSIM and an in-house dataset demonstrate the effectiveness of this approach compared to VLAs that only predict actions.", "questions": "Please check the Weaknesses", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "S1) The proposed method is well-motivated and writing is easy to follow.\n\nS2) The analysis of why employing next image prediction is beneficial, as also shown in the results of Figure 4, indicates that jointly predicting the next frame leads to more transferable features and helps avoid overfitting to dataset-specific action patterns.\n\nS3) In addition to NAVSIM, the paper also investigates the scalability of their method on a larger in-house dataset.", "weaknesses": "W1) Even though the results look good, the paradigm that jointly predicts action and future states (images) is not a new concept, both in autonomous driving (DrivingGPT, Doe-1, VaVAM) and robot learning.\n\nW2) Efficiency concern. The paper investigates both VQ and ViT based architecture. For VQ, the model is trained in an interleaved manner: image, action, image, action. The paper mentions that image generation can be bypassed during inference. However, this may lead to train-infer gap. Otherwise, the latency should be huge.\n\nW3) From Line 316-321, the model is trained in two-stage manner. In the first stage, the model is trained to predict both image and action. However, in the second stage, both VLA backbone and action expert is trained. But why only predict action in this stage. As the author already claimed that jointly predict image and action leads to better representation, will this undermine VLA's representation in the second stage?", "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": "54ycgREvnT", "reviewer": "Reviewer_uuy7", "summary": "This paper identifies a critical \"supervision deficit\" in scaling Vision-Language-Action (VLA) models for autonomous driving, where large-capacity models are underutilized due to sparse action supervision. To address this, the authors propose DriveVLA-W0, a training paradigm that incorporates world modeling as a dense, self-supervised learning signal. This approach is instantiated for both discrete (VQ-based) and continuous (ViT-based) VLA architectures using autoregressive and diffusion world models, respectively. The core contribution is the extensive experimental validation, particularly on a massive 70M-frame dataset, demonstrating that this dense supervision not only improves performance but fundamentally amplifies the data scaling law. The paper also introduces a lightweight MoE-based \"Action Expert\" to ensure real-time inference.", "questions": "The finding in Table 3 regarding the \"performance reversal\" of action experts is one of the most interesting parts of the paper. The autoregressive expert, which performs worst on the small-scale NAVSIM dataset, becomes the best-performing model at the 70M-frame scale. Could the authors elaborate on their hypothesis for this reversal? Is this purely a matter of sample efficiency, where the flow-matching expert simply requires even more data to converge on the complex trajectory manifold? Or, do the authors believe that the discrete, tokenized action space of the autoregressive model provides a fundamentally better inductive bias for modeling the highly complex and multi-modal action distributions present in a massive, diverse dataset?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "- The paper provides a clear motivation by identifying the \"supervision deficit\" as a key bottleneck. This framing is insightful and pinpoints a significant challenge in scaling VLA models.\n\n- The empirical evidence is a major strength. The authors validate their approach across two distinct VLA architectures and, most importantly, across vastly different data scales (NAVSIM and a 70M-frame in-house dataset). This large-scale study provides strong evidence for their claims.\n\n- The central claim that world modeling amplifies data scaling, rather than just providing a performance boost, can be a highly impactful finding for the driving community. \n\n- Achieving state-of-the-art results on the NAVSIM benchmark further underscores the effectiveness of the proposed representation learning paradigm.", "weaknesses": "- Training Cost Discussion: The addition of a world modeling objective (especially a diffusion-based one) presumably adds significant computational overhead to the training process. A brief discussion on the trade-offs in terms of training cost (e.g., total GPU hours vs. the baseline) would make the analysis more complete.\n\n- Generalization to Other Sensors: The work impressively achieves SOTA with a single camera. However, it would be beneficial to discuss the authors' perspective on how this world modeling approach might apply to richer sensor suites (e.g., surround-view cameras or LiDAR). Would they expect a similar \"amplification\" effect when the model already has access to more complete 3D spatial information?", "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": "dgcAvjfNOu", "reviewer": "Reviewer_dcaL", "summary": "This paper introduces DriveVLA-W0, a training paradigm for VLA models in autonomous driving that addresses the “supervision deficit” in current VLA systems, wherein large model capacity is supervised by sparse, low-dimensional action labels. DriveVLA-W0 supplements action supervision with a dense, self-supervised world modeling objective and demonstrates its method with both autoregressive and diffusion approaches. The authors conduct extensive experiments on the NAVSIM benchmark and a large-scale in-house dataset (70M frames), showing that world modeling not only accelerates performance scaling with data size but also improves generalization, efficiency, and real-time deployment feasibility.", "questions": "- In Table 2, why does equipping the world model result in worse performance on the 700k dataset compared to the baseline?\n- Can this framework be extended to incorporate multi-view image inputs?\n- Regarding latency, is the reported delay solely for the action output, or does it represent the total delay? Could you provide latency metrics with varying sequence lengths (e.g., 2VA, 3VA, etc.)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clearly define the supervision deficit issue and explain why relying solely on action objectives does not scale.\n- Introduce rigorous and balanced designs for both discrete (AR/VQ) and continuous (diffusion/ViT) visual representations, with empirical evidence showing generalization benefits under distribution shift and the critical role of dense supervision on data scaling.\n- Extensive experiments along with qualitative visualizations that link generative fidelity to planning consistency, reinforcing the claim that predictive world modeling yields richer representations.", "weaknesses": "- The claim that “the new training paradigm that uses world modeling as a powerful form of self-supervision to supplement the sparse action signal” is somewhat overclaimed, given that recent methods such as WorldVLA, DOE-1, and LAW similarly adopt self-supervised world modeling to mitigate the sparse action signal.\n- Parity of baselines on the in-house dataset is not fully convincing. The re-implementation of TransFuser differs notably in terms of pretraining compared to other approaches, and it typically uses multi-sensor input. Moreover, including more VLM-based methods would help establish a fairer comparison.\n- The experimental evaluations are conducted only in a semi-closed-loop, non-reactive setting. It would be beneficial to provide further exploration and demonstration under a fully closed-loop evaluation.", "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)