DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
Authors: Wenyao Zhang
Hongsi Liu
Zekun Qi
Yunnan Wang
XinQiang Yu
Jiazhao Zhang
Runpei Dong
Jiawei He
He Wang
Zhizheng Zhang
Li Yi
Wenjun Zeng
Xin Jin
Venue: NeurIPS 2025 poster ()
Keywords: Vision language action models
comprehensive knowledge forecasting
robot learning
Primary area: other
TL;DR: We ddvance vision-language action models via introducing comprehensive knowledge prediction.

Abstract: Recent advances in vision-language-action (VLA) models have shown promise in integrating image generation with action prediction to improve generalization and reasoning in robot manipulation.  However, existing methods are limited to challenging image-based forecasting, which suffers from redundant information and lacks comprehensive and critical world knowledge, including dynamic, spatial and semantic information.
To address these limitations, we propose DreamVLA, a novel VLA framework that integrates comprehensive world knowledge forecasting to enable inverse dynamics modeling, thereby establishing a perception-prediction-action loop for manipulation tasks. 
Specifically, DreamVLA introduces a dynamic-region-guided world knowledge prediction,  integrated with the spatial and semantic cues, which provide compact yet comprehensive representations for action planning.
This design aligns with how humans interact with the world by first forming abstract multimodal reasoning chains before acting.
To mitigate interference among the dynamic, spatial and semantic information during training, we adopt a block-wise structured attention mechanism that masks their mutual attention, preventing information leakage and keeping each representation clean and disentangled.
Moreover, to model the conditional distribution over future actions, we employ a diffusion-based transformer that disentangles action representations from shared latent features.
Extensive experiments on both real-world and simulation environments demonstrate that DreamVLA achieves 76.7 success rate on real robot tasks and 4.44 average length on the CALVIN ABC-D benchmarks.

Dream VLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
Wenyao Zhang 124+
Hongsi Liu 27-
Zekun Qi
Yunnan Wang" 12+ Jiawei He
Xingiang Yu
Jiazhao Zhang 25
Rumpei Dong
He Wang 45
Zhizheng Zhang*
Li Yia
Wenjun Zeng²
Xin Jin
SITU
EIT
THHU
Galbot
6PKU
UTUC
UUTC
Project Page
Code
Hugging Face
Abstract
Recent advances in vision-language-action (VLA) models have shown promise in integrating image generation with action prediction to improve generalization and reasoning in robot manipulation. However, existing methods are limited challenging image-basee forecasting, which suffers from redundant information and lacks comprehensive and critical world knowledge, including dynamic, spatial and semantic information. To address these limitations, we propose DreamVI.. novel VIA framework that integrates comprehensive world knowledge fore- casting to enable inverse dynamics modeling, thereby establishing a perception- prediction-acee actionn loop for manipulation tasks. Specifically, Dream VeLA introduces a dynamic-region-guun -uudded world knowledge prediction, integrated with the spatial and semantic cues, which provide compact yet comprehensive representations for action planning. This design aligns with how humans interact with the world by first forming abstract multimodal reasoning chains before acting. To mitigate interference among the dynamic, spatial and semantic information during training, we adopt a block-wise structured attention mechanism that masks their mutual attention, preventing information leakage and keeping each representation clean and disentangled. Moreover, to model the conditional distribution over future actions, we employ a diffusion-based transformer that disentangles action represen- tations from shared latent features. Extensive experiments on both real-world and simulation environments demonstrate that Dream VLA achieves 76.7% % success rate on real robot tasks and 4.44 average length on the CALVIN ABC-D benchmarks.
1 Introduction
The evolution of robot learning has demonstrated impressive progress [1-13] in training policies capable of performing diverse tasks across various environments [14-27]. One promising direction is Vision-Langinn Language-Action (VLA) models, which leverage the rich understanding capabilities of pre-trained Multimodal Large Language Models (MMLMs) [28-31] 3] to directly map natural language instructions and visual observations to robot actions [17, 1, 14]. Although these approaches [32- 34, 15, 1, 35-44] have achieved impressive results, their direct mapping from observations actions lacks the closed-loop forecasting capability that humans typically possess when understanding and reasoning about future knowledge environments.
To incorporate future knowledge prediction into VLA, most existing methods [45, 5, 46-57] leverage copilot generation model generate future frames/keypoints, then predict action sequences con- ditioned on goal images. Several methods [58-63] integrate pixel-level image forecasting with the
Equual contribution. *Coresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Death
Dynamic
Subgoal Image
World 7 Embedding
Action
Policy
Action
Action
Action
Subgoai Image
Image/ / Video Generation
VLA
VLA
DreamVLA
Image Instruction
Image
Instrutiin
Image Instruction
Image Instruction Dream Queries
(a)
(b)
(c)
(d)
Figure I: (a) Vanilla VLA directly maps visual observations and language instructions to actions. (b) Models leveraging separate image/video generation or copilot models to generate future frames or trajectories, subsequently guiding an action head. (c) VLA variants explicitly predict a subgoal image as intermediate visual reasoning step prior to action generation. (d) Our proposed Dream VLA, which explicitly predicts dynamic regions, depth map. semanties (DINOv2 and SAM) knowledge, significantly enhances the model's action reasoning and generalization.
action prediction in a single framework, which exploits the synergy of prediction and planning and regards the prediction an intermediate reasoning step [60] akin to those used in large language models (LLMs) [64]. Despite early success in incorporating dense visual forecasting, these methods naturally exhibit limitations: (1) Redundant pixel information: There exists significant overlap be- tween forecasted images and current observations, making the prediction less efficient and effective. (2) Lack of spatial information: Absence of explicit 3D knowledge of environments [65-68, 24]. (3) Lack of high-level knowledge forecasting: Missing high-level understanding of future states, e-g-- semantics information. Therefore, we argue that existing methods (Figure (a-c)) are insufficient to forecast future states for more comprehensive prediction-action loop in the context of world-level future knowledge.
To address these issues, we propose Dream VLA, a novel framework that incorporates comprehen- sive world knowledge forecasting into the Assionllagguage-cttion models, thereby establishing perception iereptioonpreecction.. prediction-actie -action loop for the manipulation task. As shown in Figure 1 (d), instead of directly generating entire future frames, our proposed method introduces world embedding predict comprehensive world knowledge, which is highly relevant to robot execution, such as dynamic area, depth, and high-level semantic features. This approach aligns with the way humans interact with the world, emphasizing relevant changes and world knowledge. By dreaming/forecasting these targeted aspects of the environment, we aim to provide the model with concise and relevant intermediate representations that facilitate more effective action planning.
To obtain comprehensive world knowledge, our approach incorporates three key features: (1) Dynamic region-based forecasting. We leverage an off-the-shelf optical flow prediction model [69, 70] to identify dynamic regions within the scene, enabling the model concentrate on areas of motion that are critical for task execution instead of redundant frame reconstruction. (2) Depth
forecasting. We employ depth estimation techniques [65] to generate per-frame depth maps. providing valuable spatial context that aids in understanding the three-dimensional structure of the environment. (3) High-level foundation features. We incorporate semantic features aligned with visual foundation models such as DINOv2 [71] and SAM [72]. In this way, Dream VLA offers a more comprehensive and effective pathway for the model to plan and execute. Furthermore, we adopt a block-wiss wise structured attention mechanism that masks their mutual attention, preventing information leakage and keeping each representation clean and disentangled. Since the world and action embeddings occupy the same latent space and share similar statistics, naive MIP head cannot disentangle modality-specifff information or exploit their cross-modal correlations. We employ a diffusion- based transformer that disentangles action representations from shared latent features to reason actions.
Through extensive experiments on public benchmarks, we find that incorporating world knowledge prediction leads to significant performance improvements. Our method achieves state-of -fee--fftheeeee -hetharrt performance on the CALVIN benchmark (4.44 average length), and we analyze the influence of the ingredients of our world knowledge and find that they have improvements in different aspects. Specifically, comprehensive ablation shows that predicting dynamic regions alone delivers the greatest gains, while depth and semantic cues offer smaller, roughly equal benefits. Worse, when depth or semantic prediction is used in isolation, it not only fails help but can actually degrade performance. Extensive experiments on both simulation and real-worke world demonstrate the effectiveness of our method.

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The key contributions of our work are summarized as follows:
We recast the vision-language-actinn the model explicitly predict a compact set of dynamic, spatial and high-level semantic information, supplying concise yet comprehensive look-ahead cues for planning.
model pereppionn-preiicioo-aattii model and make
We introduce block-wise structured-attention mechanism, coupled with cnffusiiontrrasformme decoder, to suppress representation noise from cross-type knowledge leakage and thus enable coherent multi-step action reasoning.
Dream VLA sets a new state of the art on the CALVIN ABC-D benchmark (4.44 average task length), outperforming prior methods by up 3.5% on the simulation platform, and boosts real-world success to 76.7%. Ablation studies confirm each component's contribution.
2 Related Works
2.1 Vision-Language-Actin Models
The earliest VLA [18, 73, 74-76] lay the foundation by combining pretrained vision-language representations with cask-conditioned policies for manipulation and control. Inspired by the recent advances of Large Language Models [77-80] and multimodal large language models [30, 28, 81, 67, 82] and the emergence of large-scale robot datasets [14, 83-85], VLA has become trend in robot learning. RT series [2, 86, 87] is the pioneer attempt to fine-tune the MILLM on robot demonstration datasets, resulting in strong accuracy and generalization. Building this foundation, many advanced techniques [32, 34, 15, 1, 35, 36, 75, 37-39, 88-90, 40, 91] are developed to boost the performance. Meanwhile, considering the advantage of the diffusion model in modeling multi-peak, some researchers [92-96] employ different architectures to sample action from noise conditioned on observation, task instruction, and robot prior knowledge. Given on this manner which directly maps observation and instruction to action lacks reasoning steps like LLM [64], most existing methods [45, 5, 46-51] leverage a copilot image/video generation model to generate future frames then predict action sequences conditioned on goal images. However, the above methods still need an extra generation model, which introduces inference time and computation load. Therefore, several methods [58-63] integrate pixel-Ievel forecasting with the action prediction in single framework, which exploits the synergy of prediction and planning. Despite success, these methods naturally exhibit limitations in redundant reconstruction [97], and lack spatial and semantic information.
2.2 Knowledge Forecasting for Robotics
Learning future world knowledge for robot training has increasingly become popular to enable policies for achieving an
loop. Early attempts [51, 21, 16, 45, 53, 52, 98] to implement this based on fffethe-shelf video generation models [99, 55] and feed the goal images or states into
policy model to conduct inverse dynamics. This two-stage training strategy is easy to implement but is limited by the performance and latency of video generation models. More advanced solutions couple forecasting with control by requiring the policy to produce, in addition to actions, explicit predictions. Concretely, these works ask the policy output (i) high-level subtask/option sequences or language plans that decompose long- horizon goals [100-102] (ii)latent future embeddings/latent actions that compactly encode forthcoming motor intentions [90], (iii)whoee sub-goal images or short visual rollouts that anticipate how the scene should evolve [58, 60), and (iv) object-centric signals (e.g., bounding boxes) that capture manipulation-
dynamics [85, 89]. This line of work demonstrates better performance and generalization. However, the future states are limited to redundant visual information [65, 66, 103, 71, 104, 68] or monotonous states [23, 50]. In contrast to previous work, Dream VLA proposes to predict future knowledge in an efficient (dynamic region) and effective (comprehensive knowledge) way, demonstrating strong performance and generalization.
Methodology
3.1 Problem Definition and Notation
We aim to improve robot execution by leveraging rich world knowledge as guiding principle. In this context, we formulate lision-language-uuc reasoning as inverse dynamics problem 58, 51],
3

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Training Only
Depth deen
Dynamic ferm
Semantic Open
noise
Diffusion Transformer
Decoder
Ocan-1
Frozen Trainable
World Embedding
Decoder
Decoder
Large Language Model
Visual Encoder
Encoder
Encoder
00000 00000 Set Dream Queries
00000 Action Queries
Task Instruction
Robot State &
Observatio
Figure 2: Framework Overview. Given the current robot state 8t, observation or, and language instruction, DreamVLA encodes multimodal inputs via frozen text, visual encoders and tunable state encoder. These tokens, together with learnable set of <dream> queries, are processed by large language model to produce world embedding. Three lightweight decoders then project each corresponding element of this embedding into the dynamics region fr+n. monocular depth de+n and high-level semantics & A separate <action> query draws a latent action embedding, which conditions a diffusion transformer that refines Gaussian noise into an n-step action sequence a The dashed box highlights prediction heads that are used only during training; inference skips these heads and operates directly the world embedding.
which regards the future world knowledge prediction as the intermediate reasoning for robot control, fully unleashing the synergy prediction and execution. At each time step I, the robot receives three heterogeneous signals: natural language instruction I, state s, To inject look-ahead reasoning, we define a set of special tokens called <dream> queries [8 l], and concatenate all inputs into sequence. A unified model M maps these inputs into compact latent representation, which we call the world embedding:
visual frame o,, and its proprioceptive
Wt+n M (1, Ot, st) <dr<errm>)...
Next, the world embedding predicts the comprehensive world knowledge that combines motion cues, spatial details and high-level semanties. Specifically, set of predictor extrapolates steps ahead,
Penn (ww++m) [fetns depn &x+m]
where feon marks dynamic regions, de+n encodes monocular depth, and ô,- optionally stores high-level semantic feature (e.g. DINOv2 [71]. SAM (72]).
Given world embedding Witn, the <action> query is assigned to the latent action embedding by the unified model M to aggregate the correlated action information. denoising difffusion transformer 'D formulates an n-step action based on the latent feature:
thus completing 0 D(M(1, Ot, 8t. <dream><<action>))
araticiion-actionn loop that is identical during training and inference. The remainder of this chapter details the system components- encoders, world- knowlegee predictor, and iiffusion-based action generator- that instantiate the above formulation.
3.2 Model Architecture
As illustrated in Figure our DreamVLA framework comprises three core modules operating within unified transformer architecture. Firstly, heterogeneous inputs-incluune intiisdiccinnngg natural language I, visual observations o,, and proprioceptive states 8--are individually processed by modality encoders. We encode language instructions using CLIP [103] text embeddings. visual frames through a Masked Autoencoder [106] obtain spatiotemporal patch representations, and proprioceptive signals via several convolutional and fully-connected layers. Following encoding, set of learnable

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T=0 T=10
T=20
T=30
T=O
T=10
T=20 T-30
Static view
Static view Dynamic area observation
Wrist view observation
Wrist wiw dynanic area
Figure 3: Visualization of dynamic regions over time. We show the static camera (left) and wrist-mounted camera (right) observations alongside the corresponding dynamic masks generated by our method at multiple time steps. The masks highlight dynamic regions by leveraging optical flow trajectories extracted via CoTracker [70, 69]. Compared to the original observations, our method effectively suppresses irrelevant background and focuses on interaction-relevant areas (c.g., moving objects and end-effector), enabling more structured and efficient action reasoning.
queries designated as <dream> and <action> are appended to these multimodal embeddings, where <dream> contains three subqueries (dynamic, depth and semanties), which could be used for the prediction of specific knowledge. Subsequently, we leverage large language model based on GPT-2 [107] integrate and attend across modalities and queries using carefully structured causal and non-causal attention mechanisms (Figure 4). This effectively fuses low-level perceptual signals into compact, semantically coherent representations of the world state.
Finally, specialized light-weight output heads comprising by shallow convolutional layers decode world embedding into explicit predictions: reconstruct anticipated dynamic region, monocular depth, and semantic features. During inference, DreamVLA skips the decoder entirely, saving substantial computation. Instead, the model outputs world embedding that encapsulates predictions of future dynamics, depth, and semantics without pixel-level reconstruction, thereby retaining the accuracy gains from future-state reasoning while maintaining low latency. In parallel, we employ a denoising diffusion transformer [92] to decode latent action embedding into executable robot action sequences. Collectively, these components enable Dream perform robust, predictive lasion-langageeeeee reasoning in an end-to-end manner.
3.3
Comprehensive World Knowledge Prediction
Predicting what will matter next is more valuable than merely reproducing the raw future frame. DreamVLA explicitly forecasts future world knowledge that is most relevant for manipulation, including (i) motion- centric dynamic region, (ii) 3D depth geometry, and (iii) high-level semantics. These complementary signals provide a compact, structured surrogate for raw pixels and supply the policy with look-ahead context for inverse dynamics planning.
Motion-centris dynamic-region reconstruction. Predicting dynamic regions tells the robot what parts of the scene are about to move, allowing the model to capture the statistical link between the current scene, the language instruction, and the actions needed to realize the predicted motion. As shown in Figure 3, Dream VLA neither predicts dense optical flow nor synthesizes an entire future frame. Instead, move with the robot end-effector or other movable objects, and then train Dream VLA to reconstruct only these regions. Furthermore, generating reconstruction targets with asymmetrical tokenizer can further enhance performance [106]. From the perspective of discrete variational autoencoder (dVAE) [108-111], the overall optimization is to maximize the evidence lower bound (ELBO) [112 [112- 11 114, 68] of the log-likelihodd (rr|(t ). Let x denote the original image, the masked motion region, and the reconstruction target. The generative modeling can be described as:
first apply CoTracker [69, 70] to extract dynamic regions, namely pixels that
Σ (DD
log P(x.(a)) >
Σ (x.
(Eg,-usttle)) [log (arlza)] DKL Po(le))(( (4)
where (x(2) is the tokenizer decoder to recover origin data, é; = (z(ā,) denotes the masked motion region tokens from masked data and Po (2)) reconstructs masked tokens in an autoencoding
5

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fashion. Here, the Po(z|2;) is zero, and the dynamic region prediction loss can be formulated as:
Re [- log (=.(( =)) IIED
Ldyn
(5)
D
Depth prediction. Predicting how the depth field will evolve tells the robot where should move next, steering toward free space and away from impending obstacles. If depth sensors are available, we supervise the DreamVLA with ground-truth maps; on low-cost platforms without depth sensing, instead hallucinate future geometry from single RGB stream. To do so, treat Depth-Anything [65, 66] predictions as a self-supervised teacher and train a dedicated depth query to regress the aligned future map dz+n- The objective is a scale-normalized mean-squared error,
Ldcpth
HW
Σ 1
((.J) (1.5)) ttn
ditn
(6)
where a removes the global scale ambiguity that monocular methods cannot resolve. In practice, this simple loss sufficient: the teacher provides metrically plausible depth, and the scalennoralizzation term encourages the model preserve ordinal depth relationships, a property that is crucial for grasp synthesis and collision checking, while ignoring any arbitrary global scale shift.
Contrastive semantic forecasting. Predicting future semantics teaches the robot which objects or regions will matter for the task, providing a high-level context (for example, object identity and affordances) that guides the selection of goals and grasp choice. To learn these semantics, Dream VLA predicts future DINOv2 [71] and SAM [72] feature ê+z using an InfoNCE loss [115, 68]: the ground-truth feature the positive sample, whereas spatially shifted features act negatives. This encourages discriminative anticipation that the model must pick the correct object semantics among plausible but wrong futures:
Luem log exp(cinntta//
(8)
Σn exp(eT+n (fttn Cx/T)"
where k represents the number of tokens in spatial, and 7 denotes the temperature.
Structured attention for cross-type knowledge dis- entanglement. To preserve clear cross-type knowl- edge boundaries, <dream> is decomposed into three sub-queries (dynamic, depth and semantics). If these sub-queries could freely attend one another, high- frequency flow details would contaminate depth rea- soning, and semantic cues might bleed into motion features, producing noisy mixed representations. We therefore mask their mutual attention: each sub- query attends only to the shared visual, language, and state tokens, while direct links among the three are disabled, keeping their latent features disentangled and free of cross-talk. As shown Figure 4, both <dream> and <action> queries also employ causal attention restricted to past context, which preserves temporal causality. This organized pattern mirrors the specialist routing used in Mfxture-of-Exxe -fppeeet-s-- (MoE) networks [116]. By avoiding cross-mat modal leakage, the structured attention supplies clean future world knowledge for action prediction, improves robustness, and maintains temporal consistency.
Text taken
State token
Dynamic
Depth
Semantic
Action
queries
Text token
State token
Dynamic
Depth
Semantic
Action
queries
Figure 4: Block-wise structured attention.
3.4 Inverse Dynamics via Denoising Diffusion Transformer
Given two ordered observations o, and 0t+1, classical inverse dynamics infers the intermediate action àz We extend this formulation by predicting full action sequence 0rt-o- conditioned

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Table 1: CALVIN ABC-D results. We present the average success computed 1000 rollouts for each task and the average number of completed tasks to solve 5 instructions consecutively (Avg. Len.). Dream VLA shows significant superiority over baselines. The best results are bolded.
Task completed in
Method
2
3
4
5
Avg. Len. 1
Roboflamingo [32]
82,4
61.9
46,6
33.1
23.5
2,47
Susie [120]
87.0
69.0
49.0
38.0
26.0
2.69
GR-1 [16]
85.4
71.2
59.6
49.7
3.06
Diffusor Actor [95]
92.2
78.7
63.9
51.2
3.27
OpenVILA [1]
91.3
77.8
62.0
52.1
43.5
3.27
RohoDDal [121]
94.4
82.7
72.1
62.4
54.4
3,66
UNIVLA [122]
95.5
85.8
75.4
66.9
56.5
3,80
No [34]
85.0
76.7
68.1
3.84
CLOVER [123]
96.0
83.5
70.8
57.5
45.4
3,53
UP-VLA [59]
92.8
86.5
81.5
76.9
69.9
4.08
Robovim [39]
98.0
93.6
85.4
77.8
70.4
4.25
Seer [58]
96.3
91.6
86.1
80.3
74.0
4.28
VPP [51]
95.7
91.2
86.3
81.0
75.0
4.29
Dream VLA
98.2
94.6
89.5
83.4
78.1
4.44
on the current observation O, and future latent world embeddings Wi-n- Specifically, DreamVLA first aggregates this latent emhedding, already enriched with predicted future dynamics, depth, and semantics, into a compact action embedding via a dedicated action query and the model's causal attention. Since the world and action embeddings occupy the same latent space and share similar statistics, a naive MLP head cannot disentangle modality-specifie information or exploit their cross- modal correlations. We therefore employ a denoising diffusion transformer (DiT) [92, 117] as the action head. Conditioned on the action embedding, DiT employs iterative self-attention and denoising fuse perceptual forecasts with control priors and to transform Gaussian noise into n-step trajectory Gr:++n-1- yielding coherent, diverse, and physically grounded action sequences. The loss of action prediction can be formulated as:
Pon = E,,,e/= sol Va, uuttn-1 VI- T, c)
where Eo is the DiT denoiser, & (0, I), ãt follows cosine noise schedule and c is the latent action embedding obtained from a large language model. Inference is performed by drawing Gaussian sample and running the learned reverse diffusion, yielding diverse yet physically plausible trajectories that close the perceptioon-rediition---cce loop.
4 Experiments
4.1 Implementation Details
All models are implemented in PyTorch and trained on NVIDIA 8 A800 GPUs. We use an AdamW [118] optimizer with initial learning rate 10-3 weight decay le 4, and a cosine learning- rate schedule with 5% linear warm-up. Batch size is set to 64, we set the query length of each modality and diffusion steps in DiT to 10. weight the dynamic region, depth and segmentation prediction losses =0,1= Adeph-0.0000 -0.1 and the action loss as 1 respectively. first pre-train Dream VLA on the language-free split of the CALVIN [1 [1] and on the full DROID dataset [84]. For the LIBERO benchmark, first pretrain Dream finetune on each track. The model predicts entire frames instead of comprehensive knowledge, keeping storage and computation requirements manageable. We then fine-tune Dream on each target dataset using the comprehensive world knowledge forecasting objective. All models are trained for 20 epochs, and select the checkpoint with the highest validation success rate (SR) for final evaluation.
LIBERO -90 and then
4.2 Simulation Benchmark Experiments
Simulation setup. We evaluate Dream on CALVIN [119] and LIBERO [124] benchmark. CALVIN is simulated benchmark designed for learning long-horizon, language-conditioned robot manipulation policies. It comprises four distinct manipulation environments and over six hours

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Table 2:
The extended LIBERO experiments. DreamVLA achieves the best competitive performance across all tracks compared to previous approaches. The best results are bolded.
Scores (%)
Methods
Average
Spatial
Ohject
Goal
Long
Diffusion Policy [92]
78.3
92.5
68.3
50.5
72.4
Octo [15]
78.9
85.7
84.6
51.1
75.1
Open VLA [1]
84.7
88.4
79.2
53.7
76.5
Vpa [38]
88.2
89.9
78.6
55.5
78.1
CoT-VLA [60]
87.5
91.6
87.6
69.0
81.1
Dream VLA
97.5
94.0
89.5
89.5
92.6
of teleoperated play data per environment, captured from multiple sensors including static and gripper-mounted RGB-D cameras, tactile images, and proprioceptive readings. We report the success rate every track and the average length of tasks. Additionally, evaluations are also conducted on LIBERO [124], simulated benchmark spanning four suites PABERO-Spatial/--eee Sobbect/-Gooll-Dlt Goal/-Long! demonstrations, targeting spatial
Each suite contains 10 tasks supported by 50 numan-teleoperrs reasoning, object-centric manipulation, and goal completion.
Results. As shown in Table 1, Dream VLA achieves the highest performance on ABC-D tasks, Our method surpasses Roboflamingo [32], 3D Diffusor Actor [95], OpenVLA [1], RoboDual [121] UNIVLA [122]. Robovlm [39] and GRI [16], which directly projects the RGB/depth image action signals as shown in Figure ((a) in the manuscripts. Compared to methods that use a copilot model to generate sub-goal images as input, like Susie [120] and CLOVER [123] as shown in Figure I 1(b) in manuscripts, our model significantly achieves more accurate control. Dream VLA outperforms approaches like UP-VLA [59], Seer [58], and VPP [51] as shown Figure I(c), which merge whole sub-goal image foresight into one VLA to take benefits from more integrated design and joint optimization. indicating that our method has better multi-task learning and generalization capabilities in simulation tasks. For the LIBERO benchmark [124], Dream exhibits better or comparable ability across all tracks compared to previous approaches by future world knowledge prediction as shown in Table 2.
4.3 Real World Experiments
To evaluate the effectiveness of our method in the real-world, we use the Franka Panda arm to conduct real-world experiments
gripper grasping. In setups, two RealSense D415 cameras capture RGB images. One is in third-person view, and the other is at the end of the robotic arm, as shown in Fig- ure 5. We collect four categories of objects for two tasks: pick and place. Additionally, we conduct ex- periments on drawer opening and closing tasks, as shown in the supplementary. Follow [58], we pretrain DreamVLA on the DROID [84] contains large-scale trajectories of Franka robots in varied scenes. For fair comparison, we fine-tune Diffusion Policy [92], Octo-Base [15], Open VLA [1] and Dream VLA on collected demonstration datasets containing 100 tra- jectories for each task.
RealSense D415
RealSense D415
Franka Panda
Figure 5: Real-world experiment setup.
In the experimental setup. each trial permits a max-
imum of 20 consecutive attempts. For the grasping experiments, objects are randomly positioned on the table surface. trial deemed successful if the robotic arm successfully grasps the target object within the predefined attempt limit. In the placement experiments, the robot is required to transfer the grasped object into a designated basket. Success is recorded only if both the grasping and placement operations are completed within the allowed attempts. For the drawer manipulation tasks, the drawer placed randomly in front of the robotic arm. The experiment is considered successful if the drawer displacement exceeds 10 centimeters, indicating effective interaction. The results, presented Table 3, demonstrate that our method performs better than other methods. More real-world experiment visualizations shown in the supplementary section.

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Table 3: Real-world evaluation with the Franka Robot across three tasks.
Pick
Place
Drawer
Task (All)
Method
Bottle
Doll
Avg.
Banana
Chili
Avg.
Open
Close
Avg.
Avg.
Diffusion Policy [92]
50.0
70.0
60.0
65.0
45.0
55.0
15.0
60.0
37.5
50.8
Octo-Base [15]
50.0
60.0
55.0
40.0
50.0
45.0
20.0
50.0
35.0
45.0
Ope [1]
50.0
40.0
45.0
20.0
30.0
25.0
40.0
30.0
35.0
35.0
DreamVLA
85.0
80.0
82.5
80.0
80.0
80.0
70.0
65.0
67.5
76.7
Table 4: Performance comparison between predicting the optical flow and the dynamic region.
Notably, the * denotes that this result is from [58].
Task completed a row
Method
2
3
Avg. Len.
Vanilla VLA*
93.0
82.4
72.3
62.6
53.3
3.64
+ dynamic region
97.6
92.6
87.5
80.4
73.7
4.32
+ depth
98.3
94.3
88.5
82.0
77.2
4.40
semantics
98.2
94.6
89.5
83.4
78.1
4.44
4.4 Ablation Study
In this section, we design the experiments to investigate the following questions. Q1: What is the contribution of each modal characteristic?
The core motivation of Dream VeA to enable the model to predict comprehensive visual knowledge of the future to enhance action reasoning. However, not all types of knowledge contribute equally to subsequent execution. We consider four types of predictive knowledge: dynamic region, depth, and semantic segmentation features derived from SAM and DINO. As shown in Figure 6, we first train the model with each knowledge forecasting independently. The green dashed line denotes the performance of the Vanilla VLÁ baseline, which uses no knowledge prediction. Among all, predicting dynamic regions proves be the most beneficial, because these masks explicitly flag the pixels that are about to change and therefore align almost perfectly with the policy's action semantics. By contrast, supervising the network with depth map. DINO or SAM features alone not only fails to help but often degrades performance. We analyze that this gap stems from how closely each auxiliary target matches the downstream objective: dynamic-regio that reinforce the action head, whereas depth regression and high-dimensional feature matching (DINO/SAM) inject large, noisy losses that dominate optimization. With the limited model attention budget, these competing gradients dilute the task-relevant features and push the backbone toward suboptimal optima, producing the observed drop below the dashed baseline.
labels supply gradients
Next, we train the model with all five knowledge heads simultaneously (All) and perform an ablation study (AIl-X), where we remove one knowledge signal a time to evaluate its contribution. Removing F leads to the most significant performance drop, confirming its essential role. Interestingly, removing DINO results in similar or even better performance, suggesting that not all semantic signals are equally helpful or stable in predicting outcomes, we only use semantic features from SAM in the subsequent ablations. Table reveals a clear and decreasing return pattern in all ablations.
Q2: Auxiliary Tasks vs. Future Knowledge Prediction: which drives improvement?
Table 5 contrasts two training regimes:
predicting complete world knowledge and performing auxiliary reconstructions, showing that the former is decisively superior. In our ablation, every prediction strategy is individually replaced by its reconstruction counterpart, yet each substitution consistently lowers performance: VIAA trained only to redraw the current RGB, depth, semantics, or DINOv2 features can handle the first few actions but soon loses coherence, whereas a network trained to forecast the next dynamic region, depth map, and semantics preserves accuracy throughout the trajectory and carries tasks much farther before failure. The reason is that prediction provides richer, action-on oriinned signal, directing learning toward the pixels that will drive the upcoming decision, while reconstruction merely revisits background detail that the control policy never actually needs.
Q3: Why do we use the optical flow as the mask instead directly forecasting it?
To justify our choice of employing motion-centria dynamic regions over direct flow forecasting, we implement both variants under identical settings (Table 6). the optical flow setup, the model must predict the full future flow field along with the subgoal image, which significantly increases

---

4,32
4.39
4,40
4.44
4.3
CALVIN Ave.
4.
3.74
37
3.4
3.25
298
28
Dynamic
Depth
SAM
DINO
All- Dy-Dyammic
All-Depth
All-SAM
AlI-DINO
All
Figure 6: CALVIN ABC-D performance with respect to different combinations of knowledge prediction. All=all of five models, and All-X=taking X out of All.
Table 5: Performance comparison between co- training with auxiliary tasks and predicting the comprchensive world knowledge.
Table 6: Performance comparison between pre- dicting the optical flow and dynamic region.
Method
Task completed in row
Method
Task completed in
2
3
4
5
Avg. Len.
2
4
5
Avg. Len.
Auxiliary
97.7
92.3
85.6
79.5
74.2
4.14
Optical
97.6 92.4
86.8
81.7
75.4
4.23
Prediction
98.2
94.6
89.5
83.4
78.1
4.44
Dynamic
98.2
94.6
89.5
83.4
78.1
4.44
Table 7:
Performance comparison between vanilla causal and our structured attention.
Table 8:
Performance comparison between shared and seprated queries.
Method
Task completed row
Method
Task completed row
3
4
Avg. Len.
2
Avg. Len.
Causal
94.2
86.5
71.3
62.7
3.75
Shared
95.5
90.1
83.8
76.9
70.4
4.17
Structure
98.2
94.6
89.5
83,4
78.1
4.44
Sepurated
98.2
94.6
89.5
83.4
78.1
4.44
the training complexity. This extra burden manifests markedly lower multi-step success rates. By contrast, our dynamic region approach merely employs the pretrained flow model to obtain a binary mask, focusing the model on "where" relevant motion occurs, bringing a significant improvement.
Q4: The effectiveness of structured attention in Dream VLA.
To demonstrate the effectiveness of our proposed structure attention mechanism in Figure 4, swap it for a vanilla causal mask while keeping everything else fixed. In this setting, every <dream> query, including the meant to capture semantics, same step; the extra cross-peek mixes unrelated signals, adds gradient noise, and quickly degrades long-horizon control. Our mask removes all query-to-query edges, so <action> query consults only past language, state and multimodal predictions, never their siblings. Table 7 shows the payoff: the causal variant brings marginal improvement for Vanilla VLA, whereas the block-sparse version keeps success high throughout, confirming that blocking intra-step leakage is important.
also read the flow and depth tokens produced in the
Q5: Can we use the shared query to predict the comprehensive world knowledge?
Instead of assigning separate queries to dynamic region, depth, and semantics features, one might let single set of shared queries predict all signals. To test this idea, we split each world-embedding vector into four equal sub-spaces, with each quarter intended to carry a different modality. Table shows that the shared-query design hurts action performance: mixing modalities in the same query introduces cross-talk, so the diffusion head receives noisy features. In contrast, giving each modality its query keeps the representations disentangled and yields clear performance gain.
Conclusion
We present DreamVLL a novel Hisual-Language-Acttn framework that enables inverse dynamics modeling through comprehensive world knowledge prediction, supporting the prrception-predictiin action loop for manipulation tasks. Dream VLA leverages casting, combining spatial and semantic cues to generate compact and informative representations for action planning. We introduce a block-wiss structured- attentitooon mechanism, coupled with decoder, to suppress representation noise from cross-type knowledge leakage and thus enable coherent multi-step action reasoning. Extensive experiments in both real and simu- lated environments demonstrate the effectiveness of DreamVLA achieving 76.7% success rate on real-world robot tasks and outperforming prior methods the CALVIN ABC-D benchmark.
knowledge fore-
10

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Acknowledgements
This work was supported by Grants of NSFC 62302246, ZJNSFC LQ23F010008, Ningbo 20237237 & 2024Z284 & 2024Z289 & 2023CX050011 & 2025Z038 & 20252091 and supported by High Performance Computing Center at Eastern Institute of Technology and Ningbo Institute of Digital Twin.
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Appendix
A Implementation Details
A.1 Dream VLA Architecture
Text Encoder. We use the CLIP ViT-B/32 text encoder [103] process natural language task instructions. The encoder transforms each instruction into a fixed-length embedding that captures semantic intent. These embeddings are then projected into the shared latent space and used to condition the subsequent modules, enabling effective grounding of language into perception and action.
Visual Encoder. We employ an MAE-pretrained ViT-B [106] as the vision encoder. each timestep, images are captured from two views: eye-on-hame and eye-on- base.... Each image is processed by the vision encoder to produce 196 latent vectors, which represent local patch information, along with [CLS] token that encodes the global representation of the image. Directly inputting all 197 tokens into the transformer backhone would create significant computational hurden, particularly when processing long histories. Moreover, many image details are redundant for accomplishing manipulation tasks. To address this, we utilize the Perceiver Resampler [125] to condense the image representations and extract task-relevant features. The Perceiver Resampler employs learnable latent vectors with a shape of (num latents, dim), where num latents is significantly smaller than the number of image tokens. Through Perceiver Attention. these latent vectors condense the input image features, along with the (CLS) token, form the final image tokens.
Robot State. The robot state consists of the arm and gripper state. The arm state includes the end-effecter position and its rotation in Fuler angles, resulting in six-dimensional representation. The gripper state is binary value indicating whether the gripper is open or closed. We tokenize the rohot state using an MI.P Specifically, the gripper state first converted into a one-hot encoding. The one-hot encoding the gripper state and the arm state are then each passed through separate linear layers. The outputs are concatenated and passed through final linear layer produce the state token.
Learnable Queries. introduce two sets of learnable query tokens, denoted as <dream> and <action>, to extract and integrate information from multimodal inputs for joint prediction.
The <dream> queries provide structured supervision through comprehensive knowledge prediction tasks and consist of 64 tokens in total, organized as 9 queries for each of the three modalities: dynamic motion, depth estimation and semantic features. These queries guide the model in reconstructing rich visual representations, enhancing the quality of the learned latent space.
The <action> query dedicated action sequence prediction. Their length is determined by the temporal prediction horizon, as defined in the action chunking strategy from [74].
Large Language Model. We adopt GPT-2 Medium [107] as our language backbone. GPT-2 Medium is 24-layer, 16-head Transformer decoder with hidden size of 1,024 and total of approximately 345 million parameters. was pretrained the WebText corpus (~-8 million documents. 40 GB of text) using autoregressive language modeling to predict the next token with hyte-pait encoding vocabulary of 50,257 tokens.
To decode the world embedding into comprehensive world knowledge, we incorporate multiple task-specific output heads that predict dynamic motion regions, depth maps, and high-level semantics, including DINOv2 [71] and SAMMstyyl segmentation features [72].
Output Heads.
Each prediction head implemented using lightweight Vision Transformer (ViT) decoder, which operates on two types of tokens produced by the multimodal backbone: the latent embeddings associated with a specific modality and a set of learnable mask tokens used for reconstruction.
To retain spatial correspondence. we inject fixed cosi-c positional encodings into the token embeddings. These tokens are then processed through several Transformer encoder layers, followed by modalityy-speciiii linear projection head that maps each patch token to its output space, such as per-pixel depth values semantic logits-thereby reconstructing expected visual signals of future observations. Concrete details of each module are shown in Table 9.
Action Prediction with Diffusion Transformer To generate future actions conditioned on latent action embeddigns, we adopt a diffusion based Transformer architecture, DiT-B [117], as our action decoder. DiT enables flexible modeling of complex action distributions by progressively denoising a sequence of latent action tokens through series of Transformer layers, allowing the model to capture multimodal uncertainty in robot control.
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Table 9: The parameters of the each module in Dream VLA.
Hidden size
Number of layers
Number of heads
image encoder
768
12
12
perceiver resampler
768
8
LLM image decoder
1024
24
16
1024
16
depth decoder
1024
16
semantic decoder
1024
16
We configure the DiT model with the base variant (DiT-B), using an action token embedding size equal to the hidden dimension of the fusion Transformer. The model predicts K future actions, where each action is a 7-dimensional vector that encodes the displacement of the pose and gripper state of the end effector. In our experiments,
K 2, corresponding 3-framm prediction window (current + 2 future steps). The model does not utilize past action context during generation (i.e., past window size O), focusing solely on predictive synthesis.
During training, Gaussian noise is added to the future action trajectories. and the model learns to reverse this corruption process step by step. This module operates top of the aggregated representation via <action> query, enabling temporally coherent and semantically grounded action generation. The concrete detail of DiT is shown Table 10.
Table 10: Configuration of the DīT-B model used for action prediction.
Parameter
Value
Model type
DiT-B
Token size
1024
Action prediction window
2 future steps (3-frame chunk)
Past context steps
0
Number of Transformer layers
12
Number of attention heads
12
Positional encoding
Learned (ID for time)
Diffusion timesteps (Train)
8
Diffusion timesteps (Inference)
10
Noise schedule
Linear Denoising Score Matching (L.2 loss)
Loss function
Precision
float32
A.2 Feature Extraction
To facilitate dynamic region prediction, we adopt motion-based heuristic to generate coarse binary masks that highlight regions of interest. Given sequence of consecutive RGB frames of resolution H W. uniformly sample one keypoint every pixels both spatial dimensions, resulting in = [H/8] x [W/8] sampled locations per frame. For each sampled location, we compute inter-frame displacements (Az, Ay) by tracking its position across adjacent frames using CoTracker [69]. The magnitude of displacement is converted into scalar speed value:
Sij (Ar,)) (A
where (i.j) denotes the spatial coordinates of each sampled patch. We then apply speed threshold (e.g., pixel/frame) obtain binary motion mask. To account for small motions and spatial connectivity. perform a single-pixel morphological dilation, expanding each positive location to its eight-connected neighhors.
The resulting mask is flattened and reshaped into the form (B, 1, L), where the batch size. We apply this binary mask element-wiie wise to both predicted patch embeddings {p.) and their corresponding ground mruuh--truth embeddings {p.} during loss computation, encouraging accurate representation in dynamic regions.
[H/8] [W/8] and
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For depth supervision, use the ground-truth depth maps provided by datasets when available. In cases where depth annotations not provided-sude such in certain real-world robot datasels we monocular depth estimators, specifically Depth-Anything v2 [66]. to generate geeudo-ground-trun depth labels.
addition depth and dynamic signals, we include high-level feature supervision. For DINOv2 [71], we extract features from the final transformer layer, capturing global semantic and structural representations. For SAM [72], we utilize the output of its image encoder as dense segmentation-awe aware features. These diverse modalities collectively provide comprehensive supervision signals to improve the quality and generalizability of learned visual representations.
A.3 Training Detail
The total loss can be formulated as:
Aaya + Asspu Capph + Asemhsem + ADiT
(10)
where Adya = 0.1, Adepth 0,001, Auem 0.1, ADIT
We train DreamVLA on 8 NVIDIA A800 GPUs. The main bottleneck is the memory bandwidth to load large spatial feature tensors, for example, 256x64x64 for SAM. We pre-compute the features from off-the-shelf models instead of conducting inference on the fly. This approach requires extra storage space to save all the features extracted from the above foundation models, but significantly saves training time and avoids loading models with high GPU memory usage during training. All training configurations listed in Table 11.
Table 11: Dream VLA Training Configuration
Hyperparameters
Value
GPUs
8
Batch size
8 GPU (64 effective)
Learning rate (LR)
le-3
LR Schedule
Constant
Weight decay
0.01
Optimizer
AdamW
Betas
[0.9, 0.999]
Epochs
20
Warm-up epochs
Warm-up LR schedule
Linear (Ie-2 LR)
B Experiments
B.1 Simulation Benchmark and Settings
We evaluate DLeamVLA the CALVIN benehmark [119]. simulated rohotic manipulation suite designed for studying long-horizon, language-conditioned tasks. CALVIN aims to facilitate the development of agents that operate solely hased on onhoard sensor inputs and Free-form human instructions, without access to privileged information or external supervision. The tasks in CALVIN require agents to execute long sequences of low-level control commands in response to complex language goals, reflecting realistic robotic interaction scenarios.
The benchmark includes four structurally similar but visually distinct environments. referred to as Env A, B, C. and D. Each environment features Franka Emika Panda arm with parallel gripper and tabletop workspace containing manipulable elements such sliding door, drawer, and a light button. The textures, object placements, and scene layouts vary across environments to encourage generalization and robustness.
Observations consist of RGB images from both fixed and gripper-mounte cameras (resized to 224x224), as well as low-dimensional robot state inputs that include the end-effector's 's position, orientation, and gripper status. The agent outputs 7-dimensional continuous action vector: 6 dimensions control the spatial displacement of the gripper, and the final dimension governs the open/close state of the gripper.
The dataset contains approximately 2.4 million interaction steps and 40 million short-horizon action windows. Environments A, B, and provide language-free demonstrations for large-scale pretraining, while annotated instructions are available in subset of the data for downstream policy learning. We hold out Env D for evaluation to assess zero-shot generalization to unseen combinations of instructions and environment variations.
Following standard protecol [119, 58J. evaluate performance on set of 34 diverse tasks that include object pushing, placing, rotating, and other dexterous operations. In contrast prior work, Dream not only predicts
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actions conditioned on visual-languaee observations but also simultaneously learns to infer comprehensive future world knowledge, including depth maps, dynamic saliency regions, DINOv2 features, and SAM-based segmentation maps. This multi-task supervision enables richer scene understanding and improves policy generalization. We report success rate (SR) as our primary evaluation metric, measuring whether the instructed task was completed correctly based on the final state of the environment.
B.2 Simulation Results
We evaluate our approach on the CALVIN ABC-D benchmark, where training is conducted environments A. B, and C. and lesting performed exclusively in Environment This evaluation setting poses strong challenge for generalization, as Environment D features novel textures, object arrangements, and visual configurations not seen during training. As reported in Table in the main manuscript, DreamVLA achieves superior performance across all tasks, substantially outperforming previous state-of-the-ar methods.
In particular, our model significantly outperforms two-stage inverse dynamics approaches such as Susie [120], demonstrating the effectiveness of our end-to-end architecture that unifics multimodal prediction and action generation. Compared to CLOVER [123]. UP-VLA [59], Seer [58]. which also incorporates visual foresight, Dream benefits from a more integrated design and joint optimization, resulting in consistently stronger execution accuracy. Furthermore, our method surpasses video seneration-based pretraining approaches like GR-1 [16], highlighting the advantage of coupling vision prediction with action planning in single framework.
Notably, DreamVLA, achieves average episode length of 4.44 on the ABC-D split, establishing new sfate-of-the- art on the CALVIN benchmark and validating the benefits of predicting future knowledge. The qualitative results as shown in Figure 7.
B.3
Visualization
As shown in Figure 8 and Figure 9, we visualize the model's predictions of dynamic regions and depth maps. Although supervision is applied only to dynamic regions, Dream is able to reconstruct semantically meaningful representations of the entire scene. This surprising generalization ability can be attributed to two factors. First, in long- ong-horizon manipulation sequences, the robot arm is in constant motion and frequently interacts with various objects, causing most task-relevant regions to become dynamic at some point in time. This ensures that large portion of the scene is eventually observed under dynamic supervision. Second, although static regions
explicitly supervised, the input frames inherently contain global visual context-including background structures, object appearances, and spatial layout the model can leverage to hallucinate and complete missing details. As result, Dream implicitly learns to integrate temporal dynamics with static priors, leading to coherent and accurate predictions beyond the explicitly labeled regions.
Although the predicted depth maps are relatively coarse due to the patch-level reconstruction inherent in MAE- style decoders [106]. they still provide valuable guidance for downstream tasks. In particular, the model benefits from anticipating future depth, which helps refine action decisions and improves spatial awareness.
B.4 Additional Ablation Study
Q6: Effect of the query count per modality inside <dream> queries.
Each <dream>> query contains three groups of elements: dynamic, depth, and semantics, each assigned K queries. We vary E (4, 9, 16) to examine influ-
Table 12: Performance comparison between different bers of <dream> queries.
Task completed in a row
ence. When K 4, the limited capacity prevents the model from encoding fine- grained motion, geometry, and semantics, accuracy drops even though memory us-
Number
2
Avp.
4
97.2
92.6
86.4
80.7
75.1
4.32
9
98.2
94.6
89.5
83.4
78.1
4.44
16
98.1
93.0
86.9
81.0
73.9
4,33
age is lowest. With K each modality has sufficient bandwidth without overload- ing the backbone, yielding the best success rate and the longest uninterrupted task execution. Increasing to K 16 introduces redundant tokens that compete for attention and raisc GPU memory, bringing no extra gain and slightly lower generalization.
B.5 Real-world Settings
In our real-world training setup, we use a history length of 7, with the model jointly predicting the next 3 future visual representations and action steps. The visual backbone is initialized with a VIT-B model pre-trained using MAE [106], and inference is accelerated using bfloat16 mixed precision without any observed degradation in
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Open_Drawer
Rotate Blockl
Push Into Drawer
Turn Juf__Offll Led
Lift Pink Block Slider
Figure 7: Qualitative results of the CALVIN long horizon task.
task performance. This configuration strikes a balance between computational efficiency and policy stability in manipulation tasks.
For pretraining, we leverage a large-scale dataset such as DROID [84], which contains approximately 76,000 successful robot trajectories collected in diverse settings. For downstream adaptation, we fine- tune the model
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Static Image
Static Prediction
Wrist Image
Wrist Prediction
Figure 8: Visualization results of the dynamic region predictions.
using 100 task-specific demonstrations collected with SoFar [24]. shown Figure 10, we
present the qualitative results of real-world experiments.
B.6 Inference latency
model part
inference time
image, text and state encoders
12 ms
observation forward pass w/dream query
19 ms
w/o dream query
16 ms
action forward pass (10 step)
60 ms
total
91 ms
w/o dream query
88 ms
Table 13: Inference time of our model on NVIDIA GeForce RTX 4090 GPU, test 5 times and take average time.
Table 13 reports end-to-end latency for processing two camera images on an NVIDIA GeForce RTX 4090. At inference time, explicit image decoding is required, and the system runs 11 Hz. The results show: (i) Auxiliary cues incur minimal overhead. Our "dream queries" decoding and external models). The incremental cost ms (3.4%), i.e.. ms vs. 88 ms without dream queries. (ii) Latency is dominated by the action head rather than the auxiliary cues. A 10-step action head contributes about 60 ms. This cost scales with the number of sampling steps and model size; it can be reduced by using fewer steps, smaller DiT variant, faster samplers. (iii) For latency cieeccy-rrtiical applications,
token-level predictions (no explicit image
prune
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Static Depth
Static Prediction
Wrist
Depth
Wrist Prediction
Figure Visualization results of the depth maps.
dream queries (c.g., keep only dynamic regions, or dynamic+depth) and/or increase action chunking or run the action head asynchronously to amortize computation, without changing the observation pathway.
Additional Related Works
C.1 Groundee Robot Manipulation
mnnguage-grounder robot Manipulation adopts human language as general instruction interface. Existing works can be categorized into two groups: i) End-to-end models like RT-series [2, 86, 87] built upon unified cross-modal Transformers with tokenized actions [74, 126-128, 32, 129]. large vision-language-actio
(VLA) models built from VLMs [1], 3D representations [130, 46, 131]. Training robot data such Open X-Embodiment [14] and DROID [84], remarkable process has been made. However, the data scale is still limited compared to in-the-wild data for training VLMs. ii) Decoupled high-level reasoning and low-level actions in large vision-language models and small off-the-shelf policy models, primitives [132-138, 24], or articulated priors [139, 140].
D Limitation & Future Works
While Dream VLA demonstrates solid iision-laguaage-cttion and achieves state-of-the-art performance on CALVIN [119], its current scope still narrow: it practises mainly parallel-gripeer manipulation, relies on RGB-centric data, and is trained scenes with limited geometric and material diversity. We therefore plan (i) add dexterous-Be hand demonstrations
contact annotations [141, 142], (ii) introduce point clouds [143, 144, 104, 68, 145, 146, 67, 147] and spatial information [24, 148J, tactile and fuse them into volumetric world states, and (iii) extend data collection and on on-policy fine-tuning bolster generalization and robustness.
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Pick Bottle
Pick Doll
Place Banana
Place
Chili
Figure 10: Qualitative results of real world language grouudddd manipulation.
E
Additional Discussions and Future Work
i. Scaling Laws. A promising direction for future exploration involves investigating scaling behavior in DreamVLA, In particular, we plan to study how increasing the capacity of key components such as the backhone visual encoder the size of the language afdel-affects model performance. This includes replacing the current encoder with larger-scale language models (e.g.. I.L.MA-2 GPT variants) to the impact richer linguistic understanding on multimodal reasoning action generation.
Integration with Additional Baselines. also aim to evaluate Dream VL.A conjunction with more recent and diverse baselines. For example, RoboVLMs [39] incorporate a wide range vision-language backbones and offer unified framework for robotic policy learning. Combining VLA with these baselines can help standardize performance comparisons and reveal architectural synergies between pretrained vision-language models and action-centric transformers.
iii. Contribution of Multi- View Observations. Our current framework leverages both fixed and egocentric camera views. In future work, we plan to conduct detailed ablation study to quantify the contribution of each view modality to task performance. This analysis will provide insights into how multi-view information improves spatial reasoning and robustness, especially in occluded ambiguous scenarios.
iv. Extension to More Complex and Long- Morg-oorizon Tasks. While Dream VLA demonstrates strong perfor- the CALVIN benchmark, tasks that involve extended temporal dependencies, delayed rewards, and multi-stage subgoals. This includes evaluating on benchmarks that require sustained interaction, sequential tool use, or high-level planning. Ad- dressing these challenges will require not only more powerful temporal modeling but also better integration of memory, goal abstraction, and hierarchical reasoning mechanisms.
interested in extending the framework more complex, long-horizon
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Application to Robotic Navigation and Humanoid. Beyond tabletop manipulation, Dream could be adapted robot navigation tasks in indoor semi-structured environments. By learning predict dynamic regions, obstacles, semantic scene components, the model could support instruction-drive navigation and path planning under multimodal supervision, especially settings where map-based planning is infcasible.
Furthermore, another compelling extension is applying DreamVLA to humanoid robots, which require reasoning over whole-body motion, balance, and physically grounded interactions. The modularity of our framework allows for inlegration with additional proprioceptive inputs and more complex action spaces. This line of work would explore how multimodal predictive learning can scale to full-body motor control and human-like task execution.
F
Broader Impacts
DLA proposes new training paradigm for iision-lagguuge-actionn (VLA) modeling, going beyond the conventional mapping from visual observations and language to actions. Instead of directly predicting actions from high-dimensional input, our framework first encourages the model to predict comprehensive world knowledge, including depth, dynamic motion, segmentation, and semantic features, before generating actions. This intermediate representation improves action grounding and generalization.
A key strength of Dream lies in its simplicity and efficiency: by adding only lightweight decoder and set of learnable queries, we significantly enhance the performance existing VLA backbones with minimal parameter overhead. This makes the method both scalable and compatible with current VLM-based architectures, paving the way for more robust and transferable policies.
Practically, this design can benefit the development of assistive robots' navigation and humanoid robots, where it is essential for agents to generalize across novel environments and language goals. Furthermore, since our method leverages unlabeled perceptual signals during training, reduces reliance on curated languageinnstruction datasets, which are often expensive and domain-spor -seeifi.
Overall, Dream VLA offers practical, extensible, and training- efficientf framework improving VLA systems. and we hope it inspires further research into multimodal abstraction and low-cost robot learning.
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Reviews:
[{"review_id": "gnQl3kEd56", "reviewer": "Reviewer_Pixv", "summary": "The authors proposed DreamVLA, a vision language action model for robotic manipulation that introduces additional training objectives that predict world knowledge that's relevant to robot execution, such as optical flow, depth, segmentation, and high-level semantics. The paper shows that introducing these auxiliary objectives improves policy performance on the CALVIN benchmark and the real-world environments. Ablation studies demonstrate the benefits of certain features that are being predicted.", "questions": "Why not use SigLIP or DINOv2 for visual input, which is known to improve spatial understanding compared to CLIP? It would be very helpful for robot manipulation.\n\nBatch size 8 seems really small. What is the reason not using larger one?", "limitations": "Yes.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The idea of using auxiliary objectives to improve the policy robustness is very intuitive, and the results show the benefits of some of the objectives.\n- The paper is mostly easy to read.\n\nWeaknesses:\n- The overall experiment setup is rather weak. First of all, the proposed approach is only evaluated on the CALVIN benchmark. Running additional experiments in LIBERO benchmark [1] would be helpful. Some of the conclusions, e.g., using optical flow as the mask instead of predicting it, is rather overfitted to the benchmark and not evaluated on more realistic settings such as real-world experiments. It is possible that for real-world robustness, predicting everything in the image is the most performant objective. Second, while I think ablating each individual prediction target is the most important experiment, and Table 3 showed some results, the results are not sufficient. For example, it would be better to try adding each prediction on top of the vanilla VLA and see how much performance changes, instead of incrementally adding the feature. It is possible that predicting DINOv2 itself can provide a lot of improvement while not so much when being added on top of applying dynamic mask.\n- The related work on Knowledge Forecasting for Robotics can be expanded further. For example, it will be better to explain what kind of objective “advanced solutions” also perform in “More advanced solutions [43, 41, 71, 79] incorporate forecasting and action planning in an end-to-end manner, requiring the policy to predict actions along with future states.”\n- The word “Transformer” should be used in lower case.\n\n[1] Liu et al. LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning", "was_revised": true, "final_justification": "I thank the authors for answering my questions and providing additional experimental results. My biggest concern, which was about careful ablation studies of each prediction target, has been addressed. I raised my score to 4.\n\nOverall I think this is a nice work analyzing different possible auxiliary targets in training VLAs and there are a few interesting observations (e.g., ones the authors showed in the rebuttal, so I high encourage the authors to incorporate them into the paper).", "rebuttal": "Thanks for your sincere advice. We have addressed your concerns below, and all discussions will be included in the revision.\n\n>$\\large\\textcolor{brown}{Weakness1.1:}$ **The extended LIBERO experiments.**\n\n\n| Methods  |  LIBERO-Spatial |LIBERO-OBJECT |  LIBERO-GOAL | LIBERO-LONG  | Average |\n|:----:|:-------:|:------:|:------:|:------:|:------:|\n|Diffusion Policy [72] | 78.3 | 92.5 |68.3 | 50.5 | 72.4 |\n|Octo [9]                     | 78.9 | 85.7 | 84.6 | 51.1 | 75.1 |\n| OpenVLA [1]            |84.7 | 88.4  |  79.2 | 53.7 | 76.5 |\n| SpatialVLA[31]         |88.2 | 89.9 | 78.6 | 55.5  | 78.1 |\n|**DreamVLA(Ours)**      | **97.5** |  **94.0** | **89.5** | **89.5** | **92.6** |\n\n**Analysis:**\nDue to time limitations, we only pretrain DreamVLA on LIBERO-90, and other models [1][9][31] pretrained on larger datasets, such as Open x-embodiment [8]. In this case, our DreamVLA still surpasses other methods on most tasks, especially complex (LIBERO-GOAL +4.9), long-horizon (LIBERO-LONG +34.0) tasks and average performance (+14.5), demonstrating that future world knowledge prediction could benefit robot manipulation in various tasks.\n\n>$\\large\\textcolor{brown}{Weakness1.2:}$ **Whole Image vs. Dynamic Masks in real-world?**\n\nTo test this, we ran a real-robot ablation on real experiments and two additional, contact-rich tasks (wipe board, stack bowls) and compare full-image reconstruction vs. dynamic-region prediction:\n\n| Methods         |  |     Pick    |        |  |     Place   |        |  |     Drawer   |        | Wipe | Stack | Average |\n|:------------|----------:|:-------:|:---------:|:----------:|:------:|:------:|:-----------:|:------:|:------:|:---------:|:----------:|:----------:|\n|                | Bottle| Doll| Avg.| Banana | Chili| Avg.| Open    | Close| Avg.| Board | Bowl   | Average   |\n| Image    | 80.0      | 80.0    | 80.0        | 80.0       | 70.0   | 75.0   | 65.0        | 45.0   | 55.0   | 55.0    | 45.0    | 62.0       |\n| **Dynamic** | **85.0**      | **80.0**    | **82.5**       | **80.0**       | **80.0**   | **80.0**   | **70.0**        | **65.0**   | **67.5**   | **70.0**     | **65.0**       | **73.0**       |\n\n**Analysis:**\nSimple tasks (pick and place) show no meaningful gap, but complex spatial-temporal tasks such as drawer manipulating, wiping, and stacking, favour dynamic masks by 5–20 percentage points. We attribute two factors:\n\n(i). **Higher signal-to-noise.** Gradients focus on pixels that actually move, not static background.  \n(ii). **Data efficiency.** Mask supervision is ≈80 % sparser, so each sample delivers more task-relevant signal.\n\nThese real-world results, together with our CALVIN gains over Seer [41], indicate that “predict-everything” is not automatically more robust; in complex manipulation, it can dilute learning, whereas dynamic-focused prediction transfers better to physical robots.\n\n\n>$\\large\\textcolor{brown}{Weakness1.3:}$ **The ablation study of each prediction target.**\n\nWe conduct an ablation study to investigate the effectiveness of each prediction target in CALVIN ABC->D benchmarks, where Dynamic, Depth, SAM and DINO represent only one knowledge prediction. Next, we perform an ablation study (All-X), where we remove one knowledge signal at a time to evaluate its contribution:\n\n\n| Methods  |  Task 1 | Task 2 | Task 3 | Task 4 | Task 5 | Avg. Len. ↑ \n|:----:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:| \n| Vanilla VLA | 93.0 | 82.4 | 72.3 | 62.6 | 53.3 | 3.64 |\n| Dynamic   |  97.6 | 92.6 | 87.5 | 80.4 | 73.7 | 4.32|\n| Depth   |   86.5 | 72.3 | 53.5 | 46.8 | 38.4 | 2.98|\n| SAM   |  92.0 | 77.8 | 59.0 | 52.3 | 43.9| 3.25 |\n| DINOv2   |  89.6 | 75.4 | 56.6 | 49.9 | 41.5| 3.13 |\n| ALL-Dynamic  | 94.6 | 83.3 | 73.4 | 65.4 | 57.1 | 3.74 |\n| ALL-Depth | 98.4 | 94.1 | 88.8 | 82.3 | 75.6 | 4.39 |\n| ALL-SAM   | 97.7 | 93.6 | 88.7 | 83.0 | 77.1 | 4.40 |\n| ALL-DINO  | 98.2 | 94.6 | 89.5 | 83.4 | 78.1 | 4.44 |\n---\n\n**Analysis:**\n\n(i). **Dynamic regions prediction is most important (3.64->4.32)**, because these masks explicitly flag the pixels that are about to change and therefore align almost perfectly with the policy’s action semantics. \n\n(ii) **Predicting single depth, DINO, or SAM features alone actually hurts performance.**\nDynamic masks align with the control objective, providing clear, task-relevant gradients. In contrast, depth and high-dimensional feature losses are noisy and dominate training, diluting useful signals and dragging the backbone below the baseline.\n\n(iii). **Why “DINO-only” hurt ( 3.64->3.13)?** Although several works [1][2] claim that future DINO features forecasting would benefit intermediate action reasoning, they all use two separate models. DreamVLA, in contrast, learns both visual reasoning and action generation in one backbone; a pure DINO loss drives the backbone to match appearance embeddings instead of action cues, so performance drops (–0.51). DINO is helpful as an extra signal, not as the sole objective in our unified pipeline.\n\n\n\n\n> [1]. Zhou G, Pan H, LeCun Y, et al. Dino-wm: World models on pre-trained visual features enable zero-shot planning[J]. arXiv preprint arXiv:2411.04983, 2024.\n\n> [2]. Huang Y, Zhang J I, Zou S, et al. LaDi-WM: A Latent Diffusion-based World Model for Predictive Manipulation[J]. arXiv preprint arXiv:2505.11528, 2025.\n\n>$\\large\\textcolor{brown}{Weakness2:}$ **The expanded related work.**\n\nFollowing the reviewer's suggestion, we reorganize the related works by classifying these methods according to the predicted target:\n\n*More advanced solutions couple forecasting with control by requiring the policy to produce, in addition to actions, explicit predictions about the future. Concretely, these works ask the policy to output (i) high-level subtask/option sequences or language plans that decompose long-horizon goals [1,2,3], (ii) latent future embeddings / latent actions that compactly encode forthcoming motor intentions[4], (iii) whole sub-goal images or short visual rollouts that anticipate how the scene should evolve[5,6], and (iv) object-centric signals (e.g., bounding boxes) that capture manipulation-relevant dynamics[7,8].*\n\n>[1]. Lin F, Nai R, Hu Y, et al. OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning[J]. arXiv preprint arXiv:2505.11917, 2025.\n\n>[2]. Zhou Z, Zhu Y, Zhu M, et al. Chatvla: Unified multimodal understanding and robot control with vision-language-action model[J]. arXiv preprint arXiv:2502.14420, 2025.\n\n>[3]. Shi L X, Ichter B, Equi M, et al. Hi robot: Open-ended instruction following with hierarchical vision-language-action models[J]. arXiv preprint arXiv:2502.19417, 2025.\n\n>[4]. Bu Q, Cai J, Chen L, et al. Agibot world colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems[J]. arXiv preprint arXiv:2503.06669, 2025.\n\n>[5]. Yang Tian, Sizhe Yang, Jia Zeng, Ping Wang, Dahua Lin, Hao Dong, and Jiangmiao Pang. Predictive inverse dynamics models are scalable learners for robotic manipulation. Int. Conf. Learn. Represent. (ICLR), 2024.\n\n>[6]. Qingqing Zhao, Yao Lu, Moo Jin Kim, Zipeng Fu, Zhuoyang Zhang, Yecheng Wu, Zhaoshuo Li, Qianli Ma, Song Han, Chelsea Finn, et al. Cot-vla: Visual chain-of-thought reasoning for vision-language-action models. arXiv preprint arXiv:2503.22020, 2025.\n\n>[7]. Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, DannyDriess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, et al. pi0.5: a vision-language-action model with open-world generalization. arXiv preprint arXiv:2504.16054,2025.\n\n>[8]. Shengliang Deng, Mi Yan, Songlin Wei, Haixin Ma, Yuxin Yang, Jiayi Chen, Zhiqi Zhang,Taoyu Yang, Xuheng Zhang, Heming Cui, et al. Graspvla: a grasping foundation model pre-trained on billion-scale synthetic action data. arXiv preprint arXiv:2505.03233, 2025.\n\n>$\\large\\textcolor{brown}{Weakness \\ 3:}$ **The typo of \"transformer\".**\n\nWe will revise this typo in the manuscript.\n\n>$\\large\\textcolor{brown}{Problem1:}$ **Why do not use SigLIP or DINOv2?**\n\nEmploying a more powerful visual backbone is orthogonal to our contribution. \nTherefore, we kept the visual encoder identical to Seer’s MAE [41]. \nUsing the same MAE backbone eliminates confounding factors when we compare against Seer; gains can be attributed purely to our comprehensive knowledge prediction mechanism. \nWe could integrate the SigLIP or DINOv2 encoder in the future.\n\n\n>$\\large\\textcolor{brown}{Problem2:}$ **The concern of batch size.**\n\nWe apologize for the confusion: 8 is the per-GPU batch size; with 8 A100-80 GB GPUs, the global batch size is 64."}, {"review_id": "rrV24Xn6y6", "reviewer": "Reviewer_VAuG", "summary": "DreamVLA argument the VLA pipeline with an additional world embedding apart from the action diffusion. This world embedding contains the comprhensive knowledge of the future states, which is supervised by reconstructure the future depth map, dementic map, dino feature, optical flow map. The extensive experiment on both simulation data and real world setting prove the effectiveness over the baseline models.", "questions": "1. How well does the unified world embedding reconstruct each of the four components of the future world state individually? Could the authors provide quantitative or qualitative evaluations for each?\n\n2. How is the prediction step size N selected, and how sensitive is the model’s performance to this choice?", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\n\n1. **Design of world embedding**. This unified embedding can reconstruct the multiple outputs of the future states. The abalition study also prove the necessary of \"predicting for future states\" rather than auxiliary tasks and for each role of modalities.\n\n2. The writing paper is easy to follow and clearly explain each module of the model pipeline.\n\nWeakness:\n\n1. **The success of reconstruction from world embedding** is unclear from the paper. Although the author proves that reconstructing the output for future states is helpful for the robot action prediction, how well the unified feature can decode the all four output of world states is unclear. \n\n2. What is the choose of step N?  Is the choice paprameter importat for the performance boosting?", "was_revised": true, "final_justification": "**Response to rebuttal**\n\nThank you for the thoughtful response to my previous concern, which has been fully addressed. It makes sense to me that the dynamic regions will occupy a larger portion of the scene, which can be beneficial for the robotic task, while the static regions can generalize well since they share similar backgrounds and scene observations. I also appreciate the comparison experiment on the choice of step $N$. Overall, I find the idea of improving the robotic task through future-unified features to be insightful.\n\nI raise my rate to accecpt.", "rebuttal": "Thanks for your sincere advice. We have addressed your concerns below. and all discussions will be included in the revision.\n\n>$\\large\\textcolor{brown}{Weakness 1 \\ and \\ Problem 1:}$ **The concern of success of the reconstruction.**\n\nThe dream query is internally decomposed into four sub-queries—dynamic region, depth, and semantic query, whose embeddings respectively predict future knowledge for each modality. **We have presented visualization results as shown in Fig. 1 and 2 in the supplementary material.** Although supervision is applied only to dynamic regions, DreamVLA can reconstruct semantically meaningful representations of the entire scene. This surprising generalization can be attributed to two factors. \n\n(i). The robot arm is in constant motion and frequently interacts with various objects, causing most task-relevant regions to become dynamic at some point in time. This ensures that a large portion of the scene is eventually observed under dynamic supervision. \n\n(ii). Although static regions are not explicitly supervised, the input frames inherently contain global visual context—including background structures, object appearances, and spatial layout, which the model can leverage to hallucinate and complete missing details. \n\nAs a result, DreamVLA implicitly learns to integrate temporal dynamics with static priors, leading to coherent and accurate predictions beyond the explicitly labeled regions.\n\n>$\\large\\textcolor{brown}{Weakness 2 \\ and \\ Problem 2:}$ **The prediction step size N.**\n\nThe number of future actions predicted (N) is treated as a hyperparameter. In our default configuration, we keep N = 3 following the setting adopted by Seer[41]. We add the ablation study as shown in the following table:\n| Step-N  |  Task 1 | Task 2 | Task 3 | Task 4 | Task 5 | Avg. Len. ↑ \n|:----:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|\n| 1 | 97.6 | **94.5** | 88.9 | 84.1 | 77.3 | 4.42\n| 3(default) |  **98.0** | 94.3 | **89.7** | 84.5 | **78.6** | **4.45**\n| 5 | 97.2 | 93.7 | 90.1 | **84.6** | 78.2 | 4.44\n\n**Analysis:**\nVarying the prediction horizon from 1 → 5 steps changes the average chain length by ≤0.03, i.e., well within noise.\nWe feed only the current predicted action to the simulator rather than an ensemble of future actions, so the model is largely insensitive to N. We therefore keep N = 3, following Seer [41], for all main experiments."}, {"review_id": "3iwmX3Bzd8", "reviewer": "Reviewer_gLB7", "summary": "DreamVLA recasts VLA as a closed-loop perception-prediction-action pipeline. Rather than forecasting entire image frames, it predicts a compact set of future “world knowledge” descriptors, including dynamic motion masks, monocular depth maps, and high-level semantics, via a unified Transformer. The result is a model that can generate multimodal action trajectories, yielding performance better than many previous works on simulated and real-world language-conditioned robotics tasks.", "questions": "1. Corresponding to weakness #1, I wonder if it would be reasonable to squeeze in more descriptions for many of the baseline methods in Table 1? A rough classification of them would be nice. As the authors noted in the Related Work section, there are various ways to design auxiliary tasks to facilitate policy learning. A summary of the designs adopted by the baseline methods can better illustrate the difference between them and DreamVLA\n\n2. Corresponding to weakness #2, currently, there is almost no analysis for the experiment section, except for the fact that DreamVLA has a higher success rate. Can the author provide more insight here? For example, does the failure mode change when you switch from the baseline methods to DreamVLA? Are there any characteristics of tasks that DreamVLA is especially good at? Something like this would significantly enhance the paper\n\n3. Do the authors think an additional experiment comparing with Pi0 or Pi0-Fast can be added? Pi0 represents a pretty significant step forward from Octo and OpenVLA, and is made open-source 3 months before the deadline of this NeurIPS circle.\n\n4. Finally, can the authors provide inference latency and related metrics? I understand that I may have missed it due to the sheer volume of review work I have to complete in one month. If this type of data is provided, please kindly bring it to my attention. Thank you very much!", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\n- The paper is well-written with good clarity.\n- The claim and the algorithm innovation are significant and well-supported by the exhaustive experiment design.\n- The ablation design is well thought out and covers many things I am personally curious about.\n\nWeakness:\n- In the simulation experiment session, too little description is given to all the baseline methods.\n- The analysis of simulation and real-world experiments is very limited\n- Perhaps I missed it, but I couldn’t find any information regarding inference performance (latency, etc.), which I believe is crucial for robotics applications. Specifically, this method involves many auxiliary modalities, and I wonder if they introduce latency concerns\n- The author promised that the supplementary material would include a codebase, but I couldn’t find it", "was_revised": true, "final_justification": "I believe the rebuttal provided successfully addresses the concerns I have about the manuscript. The paper itself, while it can be considered incremental, brings positive and unexplored ideas into the community.", "rebuttal": "Thanks for your sincere advice. We have addressed your concerns below. and all discussions will be included in the revision.\n\n>$\\large\\textcolor{brown}{Weakness 1 \\ and \\ Problem 1:}$ **Too little description about baseline methods.**\n\nDue to the page limitation, we provided a concrete description of them in the supplementary material.\nHere we revise to provide a brief classification and description of the baseline methods.\n\n(i). Our method surpasses Roboflamingo [26], 3D Diffusor Actor [75], OpenVLA [1], RoboDual [101], UNIVLA [103], Robovlm [32], GR1 [100], which directly projects the RGB/depth image to action signals as shown in Fig. 1(a) in the manuscripts.\n\n(ii). Compared to methods that use a copilot model to generate sub-goal images as input, like Susie [99] and CLOVER [102] as shown in Fig. 1(b) in manuscripts, our model significantly achieves more accurate control. \n\n(iii). DreamVLA outperforms approaches like UP-VLA [42], Seer [41], and VPP [40] as shown in Fig. 1(c) in manuscripts, which merge whole sub-goal image foresight into one VLA to take benefits from a more integrated design and joint optimization.\n\n\n>$\\large\\textcolor{brown}{Weakness 2 \\ and \\ Problem 2:}$ **The analysis of experiments.**\n\n\n#### 1 Failure-Mode Shift  \n| Failure category | Typical baseline error | Baseline freq. | DreamVLA freq. |\n|------------------|------------------------|---------------|----------------|\n| Perception (mis-grasp, occlusion) | Gripper closes on distractor; depth under-estimates target | **57 %** | **18 %** ↓ |\n| High-level reasoning (stall in long chain) | Policy stops after task 3 | 29 % | 10 % ↓ |\n\n**Analysis:** DreamVLA eliminates most perception- and planning-related mistakes; the few remaining errors are low-level motor slips, confirming that the predicted dynamic, depth and semantic cues effectively close the perception–reasoning gap.\n\n\n\n---\n\n#### 2 Task Length Matters  \n| Methods |  Task 1 | Task 2 | Task 3 | Task 4 | Task 5 |\n|:------:|:-------:|:-------:|:-------:|:-------:|:-------:|\n| Seer [41]   | 96.3 | 91.6 | 86.1 | 80.3 | 74.0 |\n| DreamVLA (ours) | **98.0** | **94.3** | **89.7** | **84.5** | **78.6** |\n| Δ     | +1.7 | +2.7 | +3.6 | +4.2 | **+4.6** |\n\n**Analysis:** \nThe advantage grows with horizon length—tasks 4-5 involve long-term spatial reasoning, where comprehensive knowledge offers more gain on long-horizon tasks.\nWe analyze that there are three reasons:\n\n(i). Predicts dynamic-region masks for future frames, so the Transformer keeps explicit attention on pixels that will move, rather than re-detecting them each step.\n\n(ii). Predicts a coarse future depth map, giving the policy a look-ahead of where free space/obstacles will be.\n\n(iii). The comprehensive knowledge fuses spatial and semantic cues into a shared latent, letting gradients flow from the final action back to the earliest relevant pixels.\n\n\n\n\n\n\n\n\n\n\n>$\\large\\textcolor{brown}{Weakness 3 \\ and \\ Problem 4:}$ **Latency Description.**\n\nThe following table lists the concrete latency of each component in DreamVLA on an NVIDIA GeForce RTX 4090:\n| model part                               | inference time |\n|:-----------------------------------------|---------------:|\n| image, text and state encoders           |          12 ms |\n| observation forward pass w/ dream query  |          19 ms |\n| observation forward pass w/o dream query                          |          16 ms |\n| action forward pass            |          60 ms |\n| total                               |       91 ms |\n| total w/o dream query                    |       88 ms |\n\n**Analysis**:\n\n(i). **Auxiliary modalities add a small overhead**.\nOur “dream queries” are token‑level predictions (no explicit image decoding and no external models). The incremental cost is 3 ms (3.4%) compared to w/o dream query (91 ms vs. 88 ms). \n\n(ii). **Main latency comes from the action head, not the auxiliary cues**.\nThe 10‑step action head contributes ~60 ms. This part scales roughly with the sampling steps and model size; in practice, we can \nreduce steps or use a smaller DiT variant, or adopt a faster sampler strategy or use flow policy [1]/mean flow [2,3] to further cut this cost.\n\n(iii). **Knobs to trade accuracy for speed (when needed)**.\nIf an application is extremely latency‑sensitive, we can trim dream queries (e.g., keep only dynamic regions, or dynamic+depth), and/or increase action chunking/run the action head asynchronously to amortize computation without changing the observation path.\n\n>[1]. Zhang Q, Liu Z, Fan H, et al. Flowpolicy: Enabling fast and robust 3d flow-based policy via consistency flow matching for robot manipulation[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2025, 39(14): 14754-14762.\n\n>[2]. Geng Z, Deng M, Bai X, et al. Mean flows for one-step generative modeling[J]. arXiv preprint arXiv:2505.13447, 2025.\n\n>[3]. Sheng J, Wang Z, Li P, et al. MP1: Mean Flow Tames Policy Learning in 1-step for Robotic Manipulation[J]. arXiv preprint arXiv:2507.10543, 2025.\n\n>$\\large\\textcolor{brown}{Weakness 4:}$ **Code Access.**\n\nWe will release the code.\n\n>$\\large\\textcolor{brown}{Problem 3:}$ **Comparasion with Pi0-Fast.**\n\n| Method     | LIBERO-Spatial | LIBERO-Object | LIBERO-Goal | LIBERO-Long | Average |\n|-----------------------|---------------:|--------------:|------------:|------------------:|------------:|\n| Diffusion Policy [72] | 78.3 | 92.5 | 68.3 | 50.5 | 72.4 |\n| Octo [9]              | 78.9 | 85.7 | 84.6 | 51.1 | 75.1 |\n| OpenVLA [1]           | 84.7 | 88.4 | 79.2 | 53.7 | 76.5 |\n| Pi-0-Fast [27]    | 96.4 | **96.8** | 88.6 | 60.2 | 85.5 |\n|**DreamVLA(Ours)**      | **97.5** |  94.0 | **89.5** | **89.5** | **92.6** |\n\n\n**Analysis:**\n\n(i).Pi-0-Fast leverages a powerful language backbone (Paligemma-3B) and pre-trains on a much broader dataset, whereas our current model is pre-trained only on LIBERO-90 due to time constraints. \nIn this case, DreamVLA still surpasses Pi-0-Fast on most tasks, especially complex and long-horizon tasks (LIBERO-LONG +29.3). \nPredicting compact future knowledge(dynamic region+depth+semantics) lets the policy maintain spatial-temporal awareness, whereas Pi-0-Fast must re-solve perception at every frame—small errors compound and success degrades.\n\n(ii). DreamVLA’s design is especially effective when a task requires **temporal reasoning and scene updates**, while Pi-0-Fast excels at short, isolated grasps thanks to its larger backbone. These complementary strengths suggest future work: combining DreamVLA’s future-knowledge head with a stronger LLM may yield the best of both worlds."}, {"review_id": "8q1fslyzKO", "reviewer": "Reviewer_62GB", "summary": "This paper proposes DreamVLA, a vision-language-action (VLA) framework that integrates comprehensive world knowledge forecasting and aims to establish a closed loop of action prediction through inverse dynamics modeling. This approach can be applied to predict world knowledge, thereby improving generalization and reasoning abilities in robot manipulation tasks. Its core innovation is to introduce a dynamic region-guided world knowledge prediction mechanism to jointly predict visual, depth, geometric, and semantic cues to generate a compact and comprehensive action planning representation. At the same time, the Transformer model based on diffusion is used to decouple action characterization and capture multimodal uncertainty. Experimental results demonstrate that the proposed method achieves a high success rate in real-world robot tasks, with an average task length of 4.45 on the Calvin ABC-D benchmark, significantly outperforming existing methods.", "questions": "1. Implementation details of dynamic region prediction: It is mentioned that dynamic regions are to be extracted using pre-computed optical flow; however, it is not explicitly explained how to generate binary masks from the optical flow. Are hyperparameters, such as thresholds, involved? Do these parameters affect model performance? Additional ablation experiments are recommended to verify the effectiveness of the dynamic region generation strategy.\n2. Comparison with image generation methods: The paper emphasizes that DreamVLA is more efficient than the method of generating future frames, but does not provide a direct comparison (such as computation time or memory consumption). It is suggested to supplement the efficiency comparison with copilot-like methods, such as RT-2, to quantify the 'efficiency' claim.\n3. The challenge of real-world tasks: grasping drawer operations is only tested in real experiments, whereas the generalization ability of complex scenarios, such as unstructured environments or dynamic obstacles, remains unverified. It is recommended to discuss the potential limitations of the model in a dynamically changing environment.\n4. How semantic features are used: The DINOv2 and SAM features are utilized for prediction; however, the mechanism by which they are associated with action decision-making is unclear. Does it serve only as an auxiliary representation, or does it directly affect action distribution? It is suggested to clarify its action path through visualization or attention weight analysis.", "limitations": "The authors mention that the model is limited to parallel fixtures and simple geometric scenarios. Still, the potential social implications (such as the possible ethical risks of increased robot autonomy) are not fully discussed. It is recommended that a brief analysis of security, privacy, or misuse risks be supplemented to complete the statement of research limitations.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n1. The method is rigorous in design and in-depth in theoretical analysis. The experiment encompasses both simulated and real scenarios, comprehensively comparing the baseline. The mechanism of dynamic region prediction and structured attention has technical depth, and ablation experiments verify the effectiveness of each module.\n2. The prediction mechanism of dynamic regional guidance and the design of structured attention are highly innovative\n\nWeaknesses:\n\n1. The model relies on pre-trained optical flow and depth estimation modules, which may limit the potential of end-to-end optimization. Application scenarios are limited to parallel fixture operations and geometrically simple environments, and generalization is questionable.\n2. Some technologies (such as DINOv2 feature exploitation) draw on existing work, and the boundaries of innovation need to be clearer.", "was_revised": true, "final_justification": "I appreciate the authors' responses, which have resolved nearly all of my concerns. I will keep my original score unchanged.", "rebuttal": "Thanks for your sincere advice. We have addressed your concerns below. and all discussions will be included in the revision.\n\n>$\\large\\textcolor{brown}{Weakness 1.1:}$ **The limitation of the potential of end2end optimization.**\n\nWe employ pre-trained models offline to extract dynamic-region masks, depth maps, and high-level cues before training. Because these models are not executed at inference time, they introduce zero extra runtime cost and do not interfere with the end-to-end optimization of our VLA policy.\n\n>$\\large\\textcolor{brown}{Weakness 1.2:}$ **Concern of generalization.**\n\nThese state-of-the-art flow/depth/segmentation foundation models are trained on massive datasets and exhibit excellent generalization; as shown in Fig. 3 in the manuscript, they extract accurate dynamic regions even in unseen simulated scenes.\n\n\n>$\\large\\textcolor{brown}{Weakness 2: }$ **Boundaries of innovation need to be clearer.**\n\nThanks for your suggestion, we will revise the contribution and innovation of our method in the revision.\nOur contribution is not a new segmentation or depth-estimation model. Instead, we recast the vision–language–action model as a perception–prediction–action model and make the model explicitly predict a compact set of dynamic, spatial and high-level semantic information, supplying concise yet comprehensive look-ahead cues for planning. Additionally, we introduce a block-wise structured-attention mechanism, coupled with a diffusion-transformer decoder, to suppress representation noise from cross-type knowledge leakage and thus enable coherent multi-step action reasoning.\n\n\n>$\\large\\textcolor{brown}{Problem 1:}$ **Implementation details of dynamic region prediction.**\n\nDue to space limitations, we have presented the implementation details of the dynamic region prediction in the **Feature Extraction** section of the supplementary. Specifically, given a sequence of consecutive RGB frames of resolution $H \\times W$, we uniformly sample one keypoint every 8 pixels in both spatial dimensions, resulting in $N = \\lfloor H/8 \\rfloor \\times \\lfloor W/8 \\rfloor$ sampled locations per frame. \nFor each sampled location, we compute inter-frame displacements $(\\Delta x, \\Delta y)$ by tracking its position across adjacent frames using CoTracker [50,51]. The magnitude of displacement is converted into a scalar speed value:\n$$\ns_{ij} = \\sqrt{(\\Delta x_{ij})^2 + (\\Delta y_{ij})^2}\n$$\nwhere $(i, j)$ denotes the spatial coordinates of each sampled patch. We then apply a speed threshold $\\tau$ (e.g., $\\tau = 1$ pixel/frame) to obtain a binary motion mask. To account for small motions and ensure spatial connectivity, we perform a single-pixel morphological dilation, expanding each positive location to its eight-connected neighbors.\n\n\nAdditionally, the following table shows the ablation experiments to verify the effectiveness of this threshold $\\tau$.\n| $\\tau$  |  Task 1 | Task 2 | Task 3 | Task 4 | Task 5 | Avg. Len. ↑ \n|:----:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|\n| 1(default) | **98.0** | **94.3** | **89.7** | **84.5** | **78.6** | **4.45** |\n| 2 |  97.9 | 94.1 | 89.2 | 82.8 | 76.6 | 4.41 |\n| 3 |  97.8 | 93.7 | 86.6 | 82.1 | 75.2 | 4.35 |\n\n**Analysis:**\nAs $\\tau$ increases, performance **decreases**. A higher threshold makes the dynamic‑region detector more conservative and **filters out moderate but task‑relevant motions** (e.g., early object/gripper movement). This weakens the guidance provided by predicted world knowledge and **harms action reasoning**.\n\n>$\\large\\textcolor{brown}{Problem 2:}$ **Computation time comparation.**\n\nWe measure the per‑step closed‑loop latency on an NVIDIA RTX 4090. As shown below, we compare a vanilla VLA (OpenVLA[1]), a VLA that uses a copilot model (Susie[99]) to generate a subgoal image for action reasoning, and our DreamVLA, which integrates comprehensive knowledge forecasting and action reasoning into one model. \n| Methods | Time Consuming | Avg. Len. ↑  |\n|:------|----:|---------:|\n| OpenVLA [1] | 167ms | 3.27\n| Susie [99]  |  153ms | 2.69\n| DreamVLA(ours)   |  **91ms** | **4.45**\n\n**Analysis**: Generally, VLA uses a copilot model introduces extra time-consuming to generate sub-goal images using a diffusion model, which is the main factor of low latency. \nDreamVLA achieves better performance while enabling a higher control frequency. \n\n>$\\large\\textcolor{brown}{Problem 3:}$ **Potential limitations.**\n\nDynamic obstacles moving faster than ~0.2 m/s occasionally break the frozen flow/depth priors, and large deformable objects are not yet handled.\n\n>$\\large\\textcolor{brown}{Problem 4:}$ **How semantic features are used.**\n\nWe visualize the semantic queries, including sam and dino, whose attention maps concentrate on the target objects, which demonstrate that **predicting future semantics teaches the robot which objects or regions will matter for the task**, providing a high-level context (for example, object identity and affordances) that guides the selection of goals and grasp choice."}]

Author rebuttal:

Meta-review: DreamVLA propose a vision language action model for robotic manipulation that introduces additional training objectives that predict world knowledge that's relevant to robot execution. With the world knowledge prediction ability, the proposed VLA model is able to improve the genralization and reasoning abilities. 

Alll the reviewers appreciate the technical novelties and the in-depth theoretical analysis. And the design, such as the dynamic regional guidance technically novel. The experiments and ablation studies have been well designed to support the proposed archetecure.

And the review comments from the reviewers’ have been clearly addressed. All the reviewers express positive attitude towards the acceptance of the paper.
Decision: Accept (poster)