Unified Diffusion VLA: Vision-Language-Action Model via Joint Discrete Denosing Diffusion Process
Authors: Jiayi Chen
Wenxuan Song
Pengxiang Ding
Ziyang Zhou
Han Zhao
Feilong Tang
Donglin Wang
Haoang Li
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-language-action Model
Unified Multimodal Model
Primary area: applications to robotics, autonomy, planning

Abstract: Vision-language-action (VLA) models aim to understand natural language instructions and visual observations and execute corresponding actions as an embodied agent. Recent advancements have integrated future images into the understanding-action loop, enabling foresight-driven policies that reduce abstract action prediction to a more tractable inverse kinematics problem. However, existing models either rely on external experts for modality unification or treat image generation and action prediction as separate processes, limiting the benefits of direct synergy between these tasks. In this work, we propose Unified Diffusion VLAs, which tightly couple understanding, generation, and action in a mutually reinforcing manner. Our method optimizes the generation of actions and images jointly through a synchronous denoising diffusion process, where action tokens progressively attend to future image tokens. This iterative refinement enables actions to evolve from initialization with sufficient visual guidance, ensuring precise action execution. We introduce a hybrid attention mechanism and the Joint Discrete Denoising Diffusion Process (JD3P), which integrates multiple modalities into a unified trajectory. We also propose a two-stage training pipeline and several inference-time techniques that optimize performance and efficiency. Our approach achieves state-of-the-art performance on benchmarks such as CALVIN, LIBERO, and SimplerEnv, and we demonstrate its effectiveness through ablation studies and real-world evaluations.

Published as a conference paper at ICLR 2026
UNIFIED DIFFUSION VLA: VISION-LANGUAGE- ACTION MODEL VIA JOINT DISCRETE DENOISING
DIFFUSION PROCESS
Jiayi Chen Wenxuan Song Pengxiang Ding2.3.1 Ziyang Zhou' Han 2hao
Feilong Tang* Donglin Wang" Haoang Li 1.8
'The Hong Kong University of Science and Technology (Guangzhou) Westlake University *Zhejiang University
*Monash University
ABSTRACT
Vision-languago-action (VLA) models aim to understand natural language instruc- tions and visual observations and to execute corresponding actions as an embodied agent. Recent work integrates future images into the understanding-actin yielding unified VLAs that jointly understand, generate, and act act-reading text and images and producing future images and actions. However, these models either rely on external experts for modality unification or treat image generation and action prediction separate processes, limiting the benefits of direct synergy between these tasks. Our core philosophy is to optimize generation and action jointly through synchronous denoising process, where the iterative refinement enables actions to evolve from initialization, under constant and sufficient visual guidance. We ground this philosophy in our proposed Unified Diffusion VLA and Joint Discrete Denoising Diffusion Process (JD3P), which is a joint diffusion process that integrates multiple modalities into a single denoising trajectory to serve as the key mechanism enabling understanding, generation, and acting to be intrinsically synergistic. Our model and theory are built on a unified tokenized space of all modalities and hybrid attention mechanism. We further propose a two-stage training pipeline and several inference teteence--tmeeee techniques that optimize performance and efficiency. Our approach achieves ofate-of-the-ar performance benchmarks such as CALVIN, LIBERO, and SimplerEnv with 4x faster inference than autoregressive methods, and demonstrate effectiveness through in-depth analysis and real-world evaluations. Our project is available at this url
loop,
INTRODUCTION
Vision- lanoo-language-cction models (VLAs) (Tian et al., 2025 Kim et al., 2025 Black et al. 2024) aim to understand natural language instructions and visual observations, and to execute corresponding actions as an embodied agent. In recent years, some research (Wang et al., 2025 Lv et al. 2025) has integrated future images into the understanding- acting loop to build robust. foresight
policies. This paradigm confers planning capabilities to the model by predicting future images before action inference, thereby converting the abstract task of action prediction into inverse kinematics problem. this work, we term these models as unified VLAs, which understand, generate, and act across modalities-readigg visual content and text and outputting visual content and actions.
tractable
To build unified VLAs, existing popular paradigms fall short Table Th: (1) One line of re- search (Wu et al. 2024 Tian et al., 2025 Zhang et al., (025a) unifies these modalities using extrinsic experts encoders (Dosovitskiy al. 2020, Radford et al. al.] (02T) and decoders. These VLAs encode images and language and output intermediate tokens, which serve as conditions for visual and action generation models (e.g., diffusion models) to produce future images and actions. However, such
"Equal contribution
'Project lead: songwenxuan01 1 150gma i1 com, di ngp22.0 0.1 58gma 1 com CCorrespnningg author: haoang1 @hkust terust-g..... ed..dd.cc

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modular separation often introduces misalignment, higher complexity, and weak coupling between visual generation and action prediction. (2) The other line (Zhao et al. 2025: Wang et al. 2025 Cen
et al. 2025) unifies the imput and output space through visual (Van Den Oord et al. 2017) and action tokenizers (Pertsch et al., 2025), which allows vision-language-actiinn
alignment in a token-level space and does not require extra encoders or decoders. However, in these designs, image generation and action prediction still remain separate processes, restricting the ability of the model to leverage rich future visual information for action prediction. Moreover, some of these approaches (Wang et al. 2025: Zhong et al.) 2025) only predicted images during training as an auxiliary task, thereby forfeiting the value of using future images as explicit guidance inference time. We therefore posit that genuine unification of understanding, generation, and acting requires these processes to be intrinsically synergistic, ensuring that actions are formulated as implicit mappings to the desired future observations.
One potential approach is to generate image and action tokens in an autoregressive manner. However, by design, each action token only integrates contextual information through single computation, which restricts the extent to which image generation can guide action prediction. In contrast, our core philosophy is to optimize visual generation and action prediction jointly through synchronous denoising process. Here, at every denoising iteration, all action tokens causally attend to all future image tokens, and this computation is repeated multiple times across the denoising trajectory. This iterative scheme ensures that each action token is progressively refined under sufficient guidance from future visual predictions. The coarse-to-fine refinement allows actions evolve from initialization alongside the denoising of future images, and finally converge into precise actions under a confidence- based criterion. This design effectively transforms latent visual representations into temporally structured actions.
To this end, we propose our Unified Diffusion VLA (UD-VLA) and Joint Discrete Denoising Diffusion Process (JD3P). JD3P is joint diffusion process that integrates multiple modalities into a single synchronous denoising trajectory, which serves as the key mechanism enabling understanding, generation, and acting to mutually reinforce one another. We construct our model and theory on the foundation of two techniques. (1) We unify the multimodal space through discrete tokenization, employing VQ-based visual tokenizer (Van Den Oord et al. al.) 2017: Zheng al. 2022) for images and the FAST action tokenizer (Pertsch et al.] (025) for actions. (2) We design a hybrid attention mechanism that enables rich intra-modal interactions for images and actions while enforcing causal attention across modalities, thereby preserving the nature of inverse kinematics and ensuring that action tokens are sufficiently conditioned on future visual information. For training, we conduct a two-stage pipeline to extend a pretrained VLM with image prediction capability to capture and model world dynamics in stage (i), and then apply JD3P on robot action datasets to jointly train image generation and action prediction in stage (ii). During inference, efficiency is improved through KV-cache and prefilled special tokens, candidate tokens are narrowed by remapping into a smaller range, and precision is further ensured via confidence-basee decoding.
Our method achieves state-of of-the-ar-aa-- art (SOTA) performance on multiple popular benchmarks, including CALVIN (Mees et al., 2022). LIBERO (Liu et al. 2023), and SimplerEnn (Li et al.) 2024b). while maintaining a 4x over autoregressive methods. Furthermore, in-depth analysis verifies the effective- ness of our designs, and real-world evaluations further demonstrate practical utility. In summary, our contributions are threefold:
We propose the unified diffusion VLA, which tightly couples understanding, generation, and acting in a mutually beneficial manner.
We instantiate this design via discrete tokenization, hybrid attention, and the JD3P process as the central mechanism for cross-modal synergy.
We design a two-stage training pipeline to activate the image generation capabilities and introduce several test-time techniques that ensure both high performance and efficiency.
RELATED WORKS
Visual Prediction for Robot Manipulation. Recent studies have incorporated visual prediction tasks to enhance robotic manipulation capabilities. One line of work aims to decompose the action manipulation task into visual prediction task and an inverse dynamics task. Early work Wang et al.
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Table 1: Component comparison across unified VLAs. WorldVLA and UniVLA model visual and action tokens in post-training, but decode only action tokens at inference (shown in gray text). "IBA" denotes bidirectional attention within the visual modality and the action modality separately, while they use causal attention across modalities. "CA-BA" denotes causal attention visual tokens and bidirectional attention on action tokens. "CA" denotes causal attention. For the decoding process," "-" denotes separate vision and action decoding processes. "Diff" denotes diffusion- decoding process. "AR" denotes autoregressive decoding. See Appendix Section for details of losses.
Visual Output
Action Output
Attention Mechanism
Decoding Process
Method
Loss
Expert
Loss
Expert
Unified Modalities with Extrinsic Experts
GR-1
Wu al.
2024
LMSE
LDifF-cont
CA
Diff-Dif
SEER
Tian et al. 2025
LMSR
Lritf-oont
CA
Diff-Diff
Dream VLA
Zhang ct al.
2025h
LMSE
LDIFE-com
CA
Diff-Diff
Fl
2025
LDitF-disc
CDiEE-com
IBA
Diff-Diff
UP-VLA
Zhang al.
2025a)
LDiII-ise
x
LMSE
IBA
Diff-Dift
Unified Input and Output Space in Seperate Decoding Processes
COT-VLA
Zhao et al. 2025
Loattaiie x
LiMIT-liwe
X
CA-BA
AR-Diff
WorldVLA
(Cen et al.
2025
CNTP LNTP
x
LNTP
X
CA
AR
UniVLA Wang et al.
2025)
CNTP
C
AR
Unified Input, Output in A Joint Decoding Processe's
UD-VLA (Ours)
LDur-disc
LDIIT-dicc
IBA
Diff
(2019) utilizes CIGAN Kurutach et al. (018)) to generate visual plans reference trajectory to track with a visual servoing controller, which is learned as an inverse dynamics model. Then, UniPi et al. (2023) regards the sequential decision-makee
problem text-conditioned video generation problem, which leads to a generalized, multi-task policy and naturally inherits the knowledge of video generation models. VLPDu et al. (2024) is constructed generate long video plans, including visual and language contents, which are finally transferred into aagghorizoo robot actions. To address the limited generalization caused by the small scale of robot data, [Luo al. (2025) proposes Inverse Probabilistic Adaptation to adaptively integrate in-domain information into large-scale text-to-video models.
Another line of work aims to unify the image generation and action generation in joint process. PAD et al. (2024) utilizes diffusion transformer realize the simultaneous prediction of future images and robot actions. UVA Li et al. (2025) designs joint sequence model to encode observations and actions into latent, and uses the latent to guide two separate denoising processes for videos and actions. While these methods unify the generation of images and actions through continuous diffusion process on the diffusion transformer, our UD-VLA realizes the unified generation through a discrete diffusion process on the large vasion-language model as backbone, which contributes the future research of inheriting the knowledge of the pre-trained vision-language models.
Unified VLAs. Recent work has increasingly adopted unified vision Mantonn-lnugggeaaaa action (VLA) archi- tectures, as summarized in Table I Early methods incorporated an auxiliary objective for predicting future images, employed external encoders and decoders during both training and inference unify image generation and action prediction. One line of work (Wu et al. 2024; Tian et al. 2025 Zhang al. a (2025b) supervises visual representation learning via reconstruction losses and learns action prediction through diffusion- based contrastive objectives under a causal attention mask in an au- toregressive manner. UP-VLA (Zhang et al. 2025a) pioneers a unified VLA pre-training paradigm that jointly optimizes multimodal understanding and future visual prediction, retaining semantic and spatial detail for stronger generalization and precise control. Fl (Lv et al., 2025) integrates visual foresight into the perception- generating future visual states as planning targets and guiding actions through foresight- inverse dynamics. Building advances in unified multimodal models (Wang et al. 2024 Yang et al.) 2025) Deng et al.] (025), recent VLA approaches treat inputs and outputs within a shared token space, enabling native processing of all modalities and obviating external encoders or decoders.
loop via a -ottuure-o--transffmmeenn with next-scale prediction,
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1. Architecture
3. Inference
-t F...0....c. 11 (r) AGrigge
Joint Discrete Denoising Diffusion Process (JD3P)
Unified Diffusion VLA
Fiood Length
Vartad Longth
Text Tokenizer
Image Takenizer
Training
Our Post-training (0 and Fine-tuning (ii
Image Token
Image Taken
Image
"Pick the
Traditional VLA Training
Token
Text
Image
Action
(F)
Image Token
Image Taken
Image
Action
Taken
Token
Token
Token
Figure I: Overview of our Unified Diffusion VLA. 1. We construct our UD-VLA and formalize
Joint Discrete Denoising Diffusion Process (JD3P) to allow visual generation and action prediction to be intrinsically synergistic. 2. We design two-stage training, including post-training stage in world-model manner to predict future images and fine-tuning stage to generate both future images and actions. 3. During inference, the noising fixed-length image tokens and varied-length action tokens are denoised into clean tokens after I" steps in JD3P.
COT-VLA (Zhao al. 2025 adopts diffusiio- objectives and asymmetric attention in uni- fied token space, decoding vision in an autoregressive manner and actions via diffusion, thereby making the visual chain of thought explicit through subgoal images. WorldVLA (Cen al. 2025). UniVLA (Wang et al., 2025), and Flow VLA (Zhong et al. 2023) treat both perception and control as next token under causal attention. In post-training, both visual and action tokens are modeled, while at inference only action tokens are decoded with vision serving as conditioning. However, current unified VLASs seldom unify decoding, which remains the dominant source of latency. Visual chain of thought pipelines still perform iterative image denoising and autoregressive action decoding. incurring high computational cost on both branches.
early model employing discrete diffusion inference, adopts a BART-style (Lewis et al.J (020)) denoising strategy. In this approach, subset of action tokens is randomly substituted with vocabulary tokens and then iteratively refined to recover the ground-truth sequence. In contrast, Discrete Diffusion VLA (Liang et al. 2025) and
Discrete Diffusion VLA. PD-VLA (Song et al. (025b),
LLADA-VLA (Wen al., (025) follow the BERT-style (Devlin et al. 2019) masked prediction strategy, where selected action tokens are replaced with a special [MASK] token, and the model directly learns predict the original tokens at these masked positions. To further improve efficiency, CEED-VLA (Song et al. (2025)) employs consistency distillation to reduce the number of iterations in the discrete diffusion process, leading over 4x speedup without compromising performance. However, these discrete diffusion VLAs focus exclusively on action prediction while largely ignoring the interplay between visual and action tokens, thus failing to fully exploit the potential benefits of moosl representation learning.
METHODS
We propose UD-VLA. unified diffusion VLA that bridges vision-language understanding, future image generation, and action prediction in a single transformer. We construct a unified multimodal space by quantizing multimodal information into discrete tokens (Section 3.1). Next, we design a hybrid attention mechanism to maximize the utilization of information from each modality. We then formalize our core theory, Joint Discrete Denoising Diffusion Process (JD3P), and reformulate the loss computation to support its training (Section 3.2). Finally, we balance performance and efficiency during inference through several key techniques (Section 3.3)
3.1 UNIFIED DIFFUSION VLA
Unified Tokenization. As illustrated in Figure I our method unifies language, vision, and action modalities by converting each into discrete tokens and concatenating them into single multimodal
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sequence. The language tokens follow the same design Emu3 vations are discretized with a VQ tokenizer (Zheng et al. 2022) into V. tokens in the vocabulary. and actions are represented using FAST (Pertsch et al. 2025 into V. tokens in the vocabulary. To explicitly structure the diffusion process for different modalities, we employ special tokens, <BCT> and <EOT> for images, and <BOA> and <EOA> for actions, mark the beginning and ending of their respective token sequences. Then, the complete token sequences are formatted as:
Waan et al. 2024). visual obser-
text tokens current image tokens; future image tokens action tokens] ),
(1)
where text tokens and current image tokens serve as input, and future image tokens and action tokens are output.
Hybrid Attention Mechanism. To coordinate multimodal tokens, we introduce a hybrid attention mechanism in Figure 2 The input text and current image follow causal attention and bidirectional atten- tion separately. We split output tokens into generation (future image) blocks and acting blocks. Within each block, bidirectional atten- tion enables comprehensive token interactions, which further break the time-serial dependence between action tokens avoid shorteut learning (Torne et al., 2025). Tokens across blocks are connected through causal attention: the generation block attends the input, and the acting block attends to both, while no information flows backward. This progressive design recasts the otherwise difficult objective of end-to-end action prediction in VLMs into two coupled processes: (i) foresight process that predicts the next visual state, and (ii) inverse kinematics process that infers the action condi- tioned on that visual prediction. This factorization enables principled, targeted training of both stages (see Section 3.2p. By explicitly prohibiting action-to- vision pathways, it eliminates coarse action information leakage and the attendant error compounding, improves interpretability, and ensures that downstream control is genuinely grounded in predicted visual consequences rather than spurious correlations.
Text
Cunnrt
Adton
Image
Frage
Figure 2: Hybrid attention mechanism UD-VLA.
Joint Discrete Denoising Diffusion Process (JD3P). Actions and images are generated in parallel within the same denoising step. Given the quantized future image tokens in a fixed-length sequence Vo (vo. U0.j..... Vo.L.) is Ly-dimension vector denoted by Vo.i {I...., V.} and the (Ao.1..... Cp. Go,L,) is a -imdennnsoon vector
variable-length quantized action tokens denoted by ag,; E [1...., Val, the complete sequence in JD3P is:
Vo, Ag (vo.1, L.,L., 00.1.-.., Ao.
We augment the vocabulary with special mask token M (i.e., <MASK>), yielding Vm = Vv + Va+1 symbols and one-hot basis fcc, fci,..., CV.+V. cM).
The noising process of JD3P is a Markov chain (v,, az ar)To with per-step transition matrices Q₁ E RV... VYn that independently map each token to M with probability 3, and keep it unchanged with probability 1-3, Formally, for any one-hot vector et,- of token U. and aq
Q: e,, (1-3,) et,r + +B em:
where r denotes any position in the concatenated sequence Vr, a. The transition matrices compose Q = Q ..... Q1, leading to corrupted distribution time that factorizes position-wise
(v., a Vo. an) =
II
C (VL.) or, Quen.))
(4)
where C denotes categorical distribution.
The denoising process defines conditionals pe (v a Ve, ar c) under input context (i.e., text and current image) as
Po (V Vr, c) po(v.-1 Ve, C) po(ar-1
Vt. ar, c).
(5)
Considering the mask. it can be decomposed as
Po ( Ve, c) = [0(11-15 = =v.s) 1- =M}
[ (u " Ve, ci) I(v
(6)
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and
po (0 Ve, ar, c) = (((t-1,1 = ot.s) 1-11 Aa1-15 | ( v., ae, c))] [
Here, 1 is the indicator function (1 if the condition is true, 0 otherwise), 8(-) is the Kronecker delta correspond to the model's
if the arguments are equal, otherwise), T (j -) and nh ( predictive distributions over visual and action tokens. At each denoising step, UD-VLA selectively reconstructs a subset masked positions while leaving clean others hidden, thereby annealing the mask ratio from high low until the original signals Vo and ag are recovered.
Loss Function. We discard the explicit diffusion chain and adopt single- step mask-predic objective. At each update sample mask ratio Pt (0, 1] and apply it to the clean sequences Vo and a by replacing the selected positions with <MASK>, yielding V, and a,. We then optimize the VLA pa by maximum likelihood restricted to the masked sites, computing cross-entropy only those positions to recover the original tokens.
Le
La
CCE(O) = -w
> logps (Vo.5 ce.c)-1(er) = M) L
log pe (av.s Ve, V,, c) f = M},
(8)
where w. down-weights the visual tokens avoid their dominance. This design promotes stronger vision-action interaction and replaces the multi-step corruption chain with single-step masking objective.
3.2 TRAINING
We initialize our model from a pretrained VLM backbone (Wang et al. (2024), which is trained in an autoregressive manner with causal attention. At the beginning, we adopt stage (i) to post-train UD-VLA on a large-scale video dataset, where the token sequences are constructed as:
(9) This stage injects capabilities of future image generation into the VLA, enabling the robot to understand and model future states. stage (ii), we jointly optimize image and action generation under a unified framework on the downstream robot action dataset. The token sequences are constructed as assEEqution (1) Specifically, we reformulate the autoregressive decoding as diffusion process following JD3P. Unlike standard diffusion models that predict tokens at masked positions, we adopt shift operation strategy to predict the next token, as introduced in Gong et al. (2025). This approach allows the model to retain the capacity learned from next-token prediction training, while also benefiting from bidirectional context and parallel decoding.
text tokens current image tokens future image tokens].
3.3 INFERENCE
At inference, the denoising process is instantiated through parallel decoding with adaptive masking: (i) all positions of vi and ar are initialized as <MASK>, (ii) token distributions for all positions are predicted in parallel, and (iii) this procedure is repeated for a small number of iterations.
Prefix KV Cache and Pre-filling Tokens. Our UD-VLA employs prefix KV-Cache to cache the keys and values of current visual and prompt tokens. Besides, because the length of visual tokens fixed while action tokens have varied length, we pre-fill the <BOT>, <EOT>, and <BOA> tokens the corresponding positions, which guides the denosing of visual tokens and action tokens. Our experiments empirically show that the cache and pre-filling reduce latency and contribute to higher speed.
Confidence-Gaid Guided Decoding. We initialize sequence with noise at T and iterate backward to 0, applying the cosine mask schedule P: cos smoother sampling and refinement. Follow [Equation (5) the model evaluates the joint reverse conditionals at each step. We rank only the currently masked positions and let M, C {l,..., . +L be the masked at step with size [M,]. For each location confidence score is computed:
T+1-( for T..... 1, which yields T+1
(p(el Vt, u), (pdel Ve, u), r rE (Lv + I,..,, Lo La).
E (1...., L.).
9t-1r max
(10)

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We then update the top (1 Me entries among the masked indices:
2
Topp (1~P:)[Ab)
{
Me
(1I)
For visual indices j and action indices i in Ω, tokens are updated via tempered Gumbel sampling:
"t-1j 11- GumbelMax pe Ve, ar, u), (i, E Ωr:
(12)
where GumbelMax denotes sampling using the Gumbel-max trick.
Decoding Space Mapping. The tokenized image and action tokens come from small codebooks that constitute only subset of the model's vocabulary. During inference, we restrict the classification space within each modality its designated range. This prevents the model from producing tokens of the wrong modality and stabilizes the generation process. In addition, once an <EOA> is predicted index i*, we fix the action length and deterministically replace subsequent tokens dr,i with <MASK>, preventing them from corrupting action prediction.
EXPERIMENTS
To comprchensively evaluate the effectiveness of our UD-VLA, we conduct experiments on three simulated benchmarks as well as a real-world robotic platform, comparing against widely adopted baselines and ablated methods. [Section 4.1 4.1 first introduces all benchmarks. Then, (eetion 4.2) presents results on simulated benchmarks, where our method achieves SOTA performance across diverse paradigms. Third, we provide an in-depth analysis to assess the contributions of each component in Section 4.3 At last, real-world experiments demonstrate the strong performance and generalization capability of our UD-VLA in Section 4.4
All used baselines are detailed in Section G
4.1 BENCHMARKS
CALVIN. The CALVIN benchmark (Mees et al. 2022 is a simulated suite for evaluating long- horizon, iinguage-conittoons robotic manipulation. It spans four environments (A, B, C, and D) with 34 tasks and 1,000 language instructions. We evaluate 500 rollouts per model, where each rollout involves sequence of 5 consecutive sub-tasks. We report the average length (avg. len.) of successful sub-task completions of all rollouts with a maximum value of 5.
LIBERO. LIBERO (Liu et al. 2023) is simulated manipulation benchmark with 4 suites (Spatial, Object, Goal, Long). Spatial probes layout reasoning, Object tests object generalization, Goal evaluates goal-conditioned control, and Long targets long-horizon compositional skills. We report success rates per suite and overall average, each suite containing 10 tasks and 50 rollouts per task.
SimplerEnv. SimplerEnv (L al. 2024bb is real-to-sim suite for assessing transfer and generaliza- tion of robot policies trained on real-world video data. We evaluate on WidowX robots under varied lighting, textures, colors, and viewpoints. Tasks include Put Spoon on Towel, Put Carrot on Plate, Stack Green on Yellow Block, and Put Eggplant in Yellow Basket. We report per-task success rates and the overall average.
4.2 MAIN RESULTS IN SIMULATION
CALVIN. Table 2 shows that UD-VLA achieves an average success length of 4.64 on CALVIN ABCD-> benchmark, outperforming all baselines and demonstrating strong capabilities in long- horizon reasoning and execution. Compared with UniV which autoregressively predicts future frames during post-training while only inferring actions, superior performance suggests that explicit visual generation serves as a form of chain-of-thought (CoT), providing more effective guidance for action prediction. In contrast UP-VLA, which predicts both future frames and actions in a single step on filled tokens, our multi-step diffusion more efficiently facilitates information exchange between generated images and actions.
LIBERO. Table 3 shows that UD- VLA achieves an average success rate of 96.1%, which SOTA performance the LIBERO benchmark. In particular, it attains 98.88 98.8% the Object suite and 95.2%
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Table Comprehensive Evaluation of Long-Horizon Robotic Manipulation on the CALVIN Benchmark, UniVLA* denotes the variant without historical frames for fair comparison.
Method
Task
Tasks Completed in a Row
Avg. Len
2
MCIL
(Lynch & Sermanet
2020
ABCD +D
0.373
0.027
0.002
0.000
0.000
0.40
RT-1 [Brohan el all 2023)
ABCD+DD
0.844
0.617
0.438
0.323
0.227
2.45
Robo-Flammno
Li el
2024a
ABCD-:D
0.964
0.896
0.824
0.740
0.660
4.09
GR-1 [Wu et al.
2024
ABCD-+D
0.949
0.896
0.844
0.789
0.731
4.21
Recon VLA (Song
2025c
ABCD+>D
0.980
0.900
0.845
0.785
0.705
4.23
UniVLA" |Wang al.
2025
ABCD-DD
0.948
0.906
0.862
0.834
0.690
4.26
MODE (Reuss el al. 2025)
ABCD->D
0.971
0.925
0.879
0.835
0.779
4.39
UP-VLA (Zhhng et 2002aa
ABCD-:D
0.962
0.921
0.879
0.842
0.812
4.42
MDT Reuss et al. 2024)
ABCD-$D
0.986
0.958
0.916
0.862
0.801
4.52
UD-VLA
ABCD-1 +D
0.992
0.968
0.936
0.904
0.840
4.64
Table 3: Evaluation and comparison on the LIBERO benchmark.
Method
Spatial
Object
Goal
Long
Average
Octo OOto Team al.
2024
78.9%
85.7%
84.6%
51.1 %
75.1 15.1%
S Qu
2025
88.2%
89.9%
78.6%
55.5%
78.1%
CoT-VLA Zhao et al. 2025
87.5%
91.6%
87.6%
69.0%
81.1%
T--FAST Pertsch ct al 2025
96.4%
96.8%
88.6%
60.2%
85.5%
WorldVLA (Cen
2025)
87.6%
96.2%
83.4%
60.0%
81.8%
FLO (Zhong
2025
93.2%
95.0%
91.6%
72.6%
88.1%
Dream VL. (Zhang el al.]
2025b
97.5%
94.0%
89.5%
89.5%
92.6%
OpenVLA-OFF (Kim et
2025
96.2%
98.3%
96.2%
90.7%
95.3%
UniVLA (Wang et al.
2025
95.4%
98.8%
93.6%
94.0%
95.5%
Fl (Lv et al.
2025)
98.2%
97.8%
95.4%
91.3%
95.7%
UD-YLA
96.2%
98.8%
94.2%
95.2%
96.1%
on the Long suite, demonstrating robust generalization in object recognition and strong temporal reasoning capabilities through anture-frame prediction. Our method outperforms approaches that unify modalities through extrinsic experts (e.g.. Dream VLA, F1) well as those that unify input and output spaces (e.g.. CoT-VLA, UniVLA, WorldVLA), demonstrating the advantage of our end-to-end unified VLA paradigm with joint denoising. Compared to F1, which builds a progressive generation process for future images and actions, UD-VLA delivers higher performance, highlighting that the joint discrete diffusion denoising process enables more effective fusion of the modalities.
SimplerEnv. On the SimplerEnv-Wideee Widdow benchmark (see Table 4), UD-VLA achieves an average success rate of 75.0%, significantly outperforming all baselines. Unlike methods (Qu al. 2025). that require additional inputs, UD-VLA predicts precise motion and grasp targets by explicitly modeling future frames. Our method outperforms F1, demonstrating its potential a superior paradigm for integrating understanding, generation, and acting. We also surpass UniVLA, which requires additional historical information as input, highlighting advantages of incorporating visual information into the reasoning process. In the stack block task, which requires precise manipulation, our approach achieves a 37.5% higher success rate than SpatialVLA with 3D perception capabilities.
4.3 IN-DEPTH ANALYSIS
Table Effectiveness of different atten- tion schemes. Bidirectional applies bidirec- tional attention over the visual generation and the action block. Causal uses strict lower- triangular mask and Hybrid follows Figure 2
Effectiveness of Hybrid Attention Mechanism. As shown in Table 5) the hybrid attention design achieves better performance than purely bidirectional or causal attention mechanism on CALVIN, reaching an aver- age success length of 4.64. This demonstrates that hybrid attention achieves the most effective informa- tion transfusion. Tokens in one image are able to attend to each other to ensure better global consis- tency. Likewise, actions different dimensions, such position and rotation, do not follow strict causal Hybrid (ours)
Attention
Avg. Len.
Causal Bidirectional
4.04
4.32
4.64
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Table 4: Evaluation results on SimplerEnx-Widow..
Model
Put Spoon
Put Carrot
Stack Block
Put Eggplant
Overall
OLe
Kim et al.
2025
0.0%
0.0%
0.0%
4.1%
1.0%
Octo Octo
Team al.
2024
47.2%
9.7%
4.2%
56.9%
29.5%
Robo VLMS
Liu ct
2025
45.8%
20.8%
4.2%
79.2%
37.5%
SpatialVLA
Quu ct al.
2025
16.7%
25.0%
29.2%
100%
42.7%
Fl (Lv ct al. (0255
50.0%
70.8 %
50.0%
66.7%
59,4%
Univia ( WWang al.
2025
83.3% %
66.7%
33.3%
95.8%
69.8%
UD-VLA
83.3%
62.5%
66.7%
91.6%
76.0%
Table 7: Decoding Mechanism. AR denotes autore- gressive decoding, Jacobi denotes the parallel decoding scheme used in (Song ct al.) (202b)). and ID denotes independent diffusion future images and actions.
Table 6: Target of Visual Generation. Null denotes action prediction without visual generation. Current Image de- notes reconstruct current observation.
Method
Avg. Len.
Speed (tokens/s)
Target
Avg. Len.
AR
4.18
50.2 (x1.0)
Null
4.21
Jacobi
4.16 (-0.02)
101.6 (x2.0)
Current Image
4.39 (+0.18)
ID JD3P (ours)
4.35 (40.19)
144.4 (x2.9)
Future Image (ours)
4.64 (+0.43)
4.64 (+0.46)
219.3 (x4.3)
order, and bidirectional attention enables the model to capture their correlations more effectively. However, bidirectional attention across modalities leads information leakage, as mentioned in demonstrating 0.3 lower average length.
Section 3.1
Effectiveness Future Image Generation. In Table 6 we compare three settings: not generating visual information, reconstructing current frames, and predicting future frames. While reconstructing current images improves the model's fine-grained perception and thereby strengthens action prediction, it remains limited because the model only learns static scene information. In contrast, jointly predicting future images and actions provides richer temporal cues, allowing the model to anticipate visual dynamics and align them with action planning, which leads the best performance.
Effectiveness of JD3P. As shown in Table autoregressive decoding is not well-suited for visual gen- eration, since image tokens poorly modeled by next-token generation and the process is extremely slow with only 50.2 tokens per second and a limited average length of 4.18. The Jacobi method improves decoding speed 2x, but its performance is still constrained by the inherent limitations of the autoregressive model. The independent diffusion process allows the optimization of visual generation and action prediction iteratively, which naturally suits them (Deng et al., 2025 Black al. 2024), thus reaching an average length of 4.35 and a much higher speed. In contrast, JD3P attains both the highest success rate of 4.64 and the 4.3x faster decoding speed, demonstrating that it more effective and efficient approach for unifying visual-action generation. This demonstrates that joint denoising enables the actions to better refine their predictions by leveraging image information from intermediate denoising steps, which can be regarded as computational scaling. In contrast, the independent diffusion process allows for only limited information flow, because it predicts actions with only single computation conditioned on future images.
4.4 REAL-WORLD EXPERIMENT
Setup and Tasks. The real- wolldwrldd setup is shown in in/Fugure with a detailed deseription provided in DSScctio C. We collect three categories of tasks, namely stacking bowls, putting blocks (into box), and flipping towers. Each category includes objects with varied colors and shapes, and data captured under three distinct backgrounds. For each category, record 200 trajectories at 15 Hz. During evaluation, we consider both seen and unseen settings, where unseen tasks feature novel scenes and objects.
Results and Analysis. We evaluate each methods for 30 times.
Figure 3
shows that our UD-
VLA consistently outperforms the GROOT N1 (Bjorck et al. 2025) and Uni VLA (Wang al. 2025 baselines across all tasks, achieving success rates above 80%. For seen tasks, our action quantization brings the precision of action representations, while our joint denoising process ensures the quality

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0.50
Static Camera 336L)
6-Dof (Universal UR5e)
0.80
0.63
0.80
0.83
0.40
Operational Workspace
0.50
Wrist Camera (D435))
Succeen Hale
0.59
DDDof Robotic Hand (Inspire Robots RH56E2)
Stacking
Blocks
Towers Putting Blocks
Figure 3: Real-world Setup and Results.
Generation GT
Lift the blue black (CALVIN)
Put pink block into box(Real World)
Figure 4: Visualization of Generated Future Frames and Ground-trum Frames. The leftmost frame of each trajectory represents the current observation.
of the actions. For unseen tasks, GROOT NI exhibits poor generalization on unseen targets and backgrounds, while our UD-VLA owns visual generalization to generate images with unseen targets, which further leads to correct actions. Although UniVLA also benefits from visual post- tis--training, its inference lacks explicit visual reasoning. thus falling short on unseen objects. contrast, our joint modeling of future visual tokens and actions yields markedly stronger generalization to unseen scenes, leading to higher success rates. addition, visualizations can be found in Section B
4.5 VISUALIZATION OF FUTURE IMAGE GENERATION
To intuitively illustrate the effectiveness and limitations of image generation, we present representative examples of generated future images across simulation and the real world on different embodiments in/Figure 4 Overall, the predicted frames follow instructions closely and capture task-level dynamics, remaining well aligned with the ground-truth trajectories. This consistency indicates that the model has internalized a temporal understanding of task logic, which in turn enhances temporal reasoning and enables more coherent action generation. However, the generation lacks visual fidelity, especially in fine- fine-graine grained details such as robotic arms and backgrounds. This limitation arises from the absence of large-scale generative pretraining and the use of compressed images with few tokens for efficiency. Despite these shortcomings, the generated frames reliably convey task progression and remain informative for downstream control. While pixel-level accuracy is difficult, the model consistently produces foresight images sufficient for action planning.
5 CONCLUSION
We propose unified diffusion VLA that unifies understanding, generation, and acting through joint discrete denoising diffusion process. It jointly refines image and action tokens via single synchronous denoising trajectory. We construct a unified multimodal space and a hybrid attention mechanism foundations. Besides, design two-stage training and several test-time techniques to balance performance and efficiency. Experiments show that our method achieves state-of- the-art results in both simulation and real-world environments.
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ACKNOWLEDGMENT
This work was supported in part by the Natural Science Foundation of China under Grant 62403401, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2024A 1024A151501 1992. in part by the Guangdong Provincial Project under Grant 2024QNIIXI27, and in part by the Al Research and Learning Base of Urban Culture under Grant 2023WZJD008.
REPRODUCIBILITY STATEMENT
The paper and appendix detail the full experimental setup, including data, action/image tokenization, model architectures, training schedules, action chunk lengths, decoding methods, and selection criteria (see (Section 4, |Section C and [Section F). Evaluation protocols are specified for both benchmarks, strictly following the official splits. All datasets used are publicly available, enabling consistent and verifiable evaluation. These details are provided to enable re-implementation and verification of our results.
REFERENCES
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al. Do as can, not as i say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.0169) 2022.
Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, et al. Gr0Ot nl: An open foundation model for generalist humanoid robots. arXiv preprint arXiv:2503.1 14734, 2025.
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. No: A vision-language-actinn flow model for general robot control. arXiv preprint arXiv:2410.241644 2024.
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, et al. Rt-1: Robotics transformer for real-world control at scale. Robotics: Science and Systems Foundation, 2023.
Jun Cen, Chaohui Yu, Hangjie Yuan, Yuming Jiang, Siteng Huang, Jiayan Guo, Xin Li, Yibing Song, Hao Luo, Fan Wang, al. Worldvla: Towards autoregressive action world model. arXiv preprint 0rXiv:2506.21539 2025.
Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, Guang Shi, and Haoqi Fan. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505. /4683, 2025.
Jacob Devlin, Ming-Woi Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume (long and short papers), pp. 4171-4186, 2019.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale, In International Conference on Learning Representations, 2020.
Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, and Pieter Abbeel. Learning universal policies via text-guided video generation. Advances in neural information processing systems, 36:91 11111666.7222 2023.
Yilun Du, Mengjiao Yang, Pete Florence, Fei Xia, Ayzaan Wahid, Brian Ichter, Pierre Sermanet, Tianhe Yu, Pieter Abbeel, Joshua B Tenenbaum, et al. Video language planning. In International Conference Learning Representations, 2024.

---

Published as a conference paper at ICLR 2026
Shansan Gong, Shivam Agarwal, Yizhe Zhang, Jiacheng Ye, Lin Zheng, Mukai Li, Chenxin An, Peilin Zhao, Wei Bi, Hao Peng, et al. Scaling diffusion language models via adaptation from autoregressive models. In 13th International Conference Learning Representations. ICLR 2025, pp. 47445-47472 International Conference on Learning Representations, ICLR, 2025.
Yanjiang Guo, Yucheng Hu, Jianke Zhang, Yen-Jen Wang. Xiaoyu Chen, Chaochao Lu, and Jianyu Chen. Prediction with action: Visual policy learning via joint denoising process. Advances in Neural Information Processing Systems, 37:1 12386-112400. 2024.
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Pannag R Sanketi, Quan Vuong, et al. Openvla: An open-source vision-language-action model. In Conference on Robot Learning, pp. 2679-2713. PMLR, 2025.
Thanard Kurutach, Aviv Tamar, Ge Yang. Stuart J Russell, and Pieter Abbeel. Learning plannable representations with causal infogan. Advances in Neural Information Processing Systems, 31, 2018.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. Bart: Denoising soquencc-to-sqqueme pre-training for natural language generation, translation, and comprehension. In I Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp.
2020.
Shuang Li, Yihuai Gao, Dorsa Sadigh, and Shuran Song. Unified video action model. arXiv preprint arXiv:2503.00200, 2025.
Xinghang Li, Minghuan Liu, Hanbo Zhang, Cunjun Yu, Jie Xu, Hongtao Wu, Chilam Cheang, Ya Jing, Weinan Zhang, Huaping Liu, et al. Vision-language foundation models as effective robot imitators. 2024a.
Xuanlin Li, Kyle Hsu, Jiayuan Gu, Karl Pertsch, Oier Mees, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sieh, Sean Kirmani, Sergey Levine, Jiajun Wu, Chelsea Finn, Hao Su, Quan Vuong, and Ted Xiao. Evaluating real-world robot manipulation policies in simulation. arXiv preprint arXiv:2405. 05941, 2024b.
Zhixuan Liang, Yizhuo Li, Tianshuo Yang, Chengyue Wu, Sitong Mao, Liuao Pei, Xiaokang Yang. Jiangmiao Pang, Yao Mu, and Ping Luo. Discrete diffusion vla: Bringing discrete diffusion to action decoding in lssion-language-action policies. arXiv preprint arXiv:2508. 20072, 2025.
Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning. arXiv preprint arXiv:2306.033100 2023.
Huaping Liu, Xinghang Li, Peiyan Li, Minghuan Liu, Dong Wang, Jirong Liu, Bingyi Kang, Xiao Ma, Tao Kong, and Hanbo Zhang. Towards generalist robot policies: What matters in building Dision-languagc-attion models. 2025.
Calvin Luo, Zilai Zeng, Yilun Du, and Chen Sun. Solving new tasks by adapting internet video knowledge. In International Conference on Learning Representations. 2025.
Qi Lv. Weijie Kong, Hao Li, Jia Zeng, Zherui Qiu, Delin Qu, Haoming Song, Qizhi Chen, Xiang Deng, and Jiangmiao Pang. Fl: A vision-languaae-accion model bridging understanding and generation actions. arXiv preprint wrxXh:25509069955 2025.
Corey Lynch and Pierre Sermanet. Language conditioned imitation learning over unstructured data. arXiv preprint wrXiv:2005.07648 2020.
Oier Mees, Lukas Hermann, Erick Rosete- Rosete-Beas, and Wolfram Burgard. Calvin: A benchmark for Lunguage-conditione policy learning for long- robot manipulation tasks. IEEE Robotics and Automation Letters (RA-L). 7(3):7327-7334, 2022.
Octo Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Charles Xu, Jianlan Luo, Tobias Kreiman, You Liang Tan, Pannag Sanketi, Quan Vuong, Ted Xiao, Dorsa Sadigh, Chelsea Finn, and Sergey Levine. Octo: An open-source generalist robot policy. In Proceedings of Robotics: Science and Systems, Delft, Netherlands, 2024.
12

---

Published as a conference paper at ICLR 2026
Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. Fast: Efficient action tokenization for ssion-languaag--cttin models. arXiv preprint arXlv:2501.09747, 2025.
Delin Qu, Haoming Song, Qizhi Chen, Yuanqi Yao, Xinyi Ye, Yan Ding, Zhigang Wang. Jia Yuan Gu, Bin Zhao, Dong Wang, et al. Spatialvla: Exploring spatial representations for visual-language- action model. arXiv preprimi arXiv:2501. 15830. 2025.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. International conference on machine learning, pp- 8848-8766 PmLR, 2021.
Moritz Reuss, Ömer Erdinę Yagmurlu, Fabian Wenzel, and Rudolf Lioutikov. Multimodal diffusion transformer: Learning versatile behavior from multimodal goals. Robotics: Science and Systems XX. Ed.: D. Kulic, 2024.
Moritz Reuss, Jyothish Pari, Pulkit Agrawal, and Rudolf Lioutikov. Efficient diffusion transformer policies with mixture expert denoisers for multitask learning. In The Thirteenth International Conference Learning Representations, 2025.
Wenxuan Song, Jiayi Chen, Pengxiang Ding, Yuxin Huang, Han Zhao, Donglin Wang, and Haoang Li. Ceed-vla: Consistency vision-language-actinn model with early-exit decoding. arXiv preprint arXiv:2506. 13725, 2025a.
Wenxuan Song, Jiayi Chen, Pengxiang Ding, Han Zhao, Wei Zhao, Zhide Zhong, Zongyuan Ge, Jun Ma, and Haoang Li. Accelerating iisionllanguage-action model integrated with action chunking via parallel decoding. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025b.
Wenxuan Song, Ziyang Zhou, Han Zhao, Jiayi Chen, Pengxiang Ding, Haodong Yan, Yuxin Huang, Feilong Tang, Donglin Wang, and Haoang Li. Reconvla: Reconstructive jisionlanguaageacttion model as effective robot perceiver. arXiv preprint 250iv:2508. 10333, 2025c.
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. The Thirteenth Inter- national Conference on Learning Representations, 2025. URL
Zorumm?ddmeRRcuujjnn
Marcel Torne, Andy Tang, Yuejiang Liu, and Chelsea Finn. Learning long-context diffusion policies via past-token prediction. 2025.
Aaron Van Den Oord, Oriol Vinyals, al. Neural discrete representation learning. volume 30, 2017.
Angelina Wang, Thanard Kurutach, Kara Liu, Pieter Abbeel, and Aviv Tamar. Learning robotic manipulation through visual planning and acting. arXiv preprint arXiv: vexiv:/905. 1905.04511,, 2019.
Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang. Zhen Li, Qiying Yu, al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409. 18869, 2024.
Yuqi Wang, Xinghang Li, Wenxuan Wang, Junbo Zhang, Yingyan Li, Yuntao Chen, Xinlong Wang. and Zhaoxiang Zhang. Unified vision- laano--annaaggatttiini model. arXiv preprint arXiv:2506. 19850, 2025.
Yuqing Wen, Hebei Li, Kefan Gu, Yucheng Zhao, Tiancai Wang, and Xiaoyan Sun. Llada-vla: Vision language diffusion action models. arXiv preprint arXiv:2509.06932 2025.
Hongtao WU, Ya Jing, Chilam Cheang, Guangzeng Chen, Jiafeng Xu, Xinghang Li, Minghuan Liu, Hang Li, and Tao Kong. Unleashing large-scale video generative pre-training for visual robot manipulation. In International Conference on Learning Representations, 2024.
Ling Yang, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, and Mengdi Wang. Mmada: Multimodal large diffusion language models. arXiv preprint arXiv:2505. 15809, 2025.
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Jianke Zhang, Yanjiang Guo, Yucheng Hu, Xiaoyu Chen, Xiang Zhu, and Jianyu Chen. Up-vla: A unified understanding and prediction model for embodied agent. arXiv preprint arXiv:2501.1880 2025a.
Wenyao Zhang, Hongsi Liu, Zekun Qi, Yunnan Wang, Xinqiang Yu, Jiazhao Zhang, Runpei Dong, Jiawei He, He Wang, Zhizheng Zhang, et al. Dreamvla: a vision-language-actioo model dreamed with comprehensive world knowledge. arXiv preprint arXiv:2507.04447. 2025b.
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. pp. 1702-1713, 2025.
Chuanxia Zheng, Tung-Long Vuong, Jianfei Cai, and Dinh Phung. Movq: Modulating quantized vectors for high-fidelity image generation. Advances in Neural Information Processing Systems, 35:23412-234255 2022.
Zhide Zhong, Haodong Yan, Junfeng Li, Xiangchen Liu, Xin Gong, Wenxuan Song, Jiayi Chen, and Haoang Li. Flowvla: Thinking in motion with a visual chain of thought. arXiv preprint arXiv:2508. 18269, 2025.
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Seen Objects
Unseen Objects
Target Objects
Seen Backgrounds
Distractors
System Overview
Object Splits
Unseen Backgrounds
The overview of the systems, including target objects that the robot is expected interact with, and distractors that are used to interfere with the model's actions. Middle: The objects are divided into seen ones that exist in the training sets and unseen ones that are used for the unseen evaluation of "putting blocks". Right: The backgrounds divided into and unseen, where the unseen background is used for the unseen evaluation of "putting blocks".
Figure 5: Details of our real-world experiment settings. Left:
A
LLM USAGE STATEMENT
We used large language models only general writing aids for light proofreading and language refinement in grammar, phrasing, and minor style. The research ideas, problem formulation, method- ology, experiments, analyses, code, and substantive writing were conceived and authored by us. No large language model generated new content beyond localized edits, and none was used to design experiments, analyze data, write code, or draft sections. All text was reviewed and verified by the authors, who take full responsibility for the paper, and the large language model should not be regarded as a contributor.
B VISUALIZATION OF FUTURE IMAGE GENERATION
To intuitively illustrate the effectiveness and limitations of image generation, we present representative examples of generated future images across simulation and the real world on different embodiments Figure 4 Overall, the predicted frames follow instructions closely and capture task-level dynamics, remaning well aligned with the ground-truth trajectories. This consistency indicates that the model has internalized a temporal understanding of task logic, which in turn enhances temporal reasoning and enables more coherent action generation. However, the generation lacks visual fidelity, especially in fine-grained details such as robotic arms and backgrounds. This limitation arises from the absence of large-scale generative pretraining and the use of compressed images with few tokens for efficiency. Despite these shortcomings, the generated frames reliably convey task progression and remain informative for downstream control. While pixel-level accuracy difficult, the model consistently produces foresight images sufficient for action planning.
C REAL-WORLD SETUP
Our real-world setup consists of a 6-DoF UR5e robotic arm equipped with a 6-DoF Inspire RH56E2 robotic hand for dexterous manipulation. wrist-mounted Intel RealSense D435i depth camera provides close-range RGB-D observations of the manipulated objects, while static Gemini 336L camera captures the entire operational workspace from fixed viewpoint.
In Figure we first overview our systems, including target objects that the robot is expected to interact with, and distractors. Then, we illustrate the unseen settings in detail, including unseen objects and unseen background that do not exist in the training set. The unseen settings pose challenge for the visual generalization the models. this case, our model can generate reliable future images as visual plans and further guide precise action generation, thus outperforming baselines.
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Put the pink block into the box
Stack the green bowl on the yellow bowl
Fold the
Figure 6: Visualization of tasks. Each row shows a representative trajectory of one task.
tiny toy
Put the tiny toy into the box
Figure Visualization of tasks that require precise manipulation. The tiny toy used in these experiments has radius of approximately 1.5 cm, which is about one-fifth the size of the other objects.
D REAL-WORLD TASKS
Task Definition. We carefully introduce three real-world tasks and provide visualizations in
Figure 6
Stacking Bowls: The robot expected to lift one bowl and stack it on the other.
Putting Blocks: The robot is expected to lift the target block and put it into the box.
Flipping Towers: The robot is expected to grasp one side of the tower and move to the other side, until the towels are folded.
Training Details. We train our model for real-world experiments on NVIDIA H100 GPUs for 24 hours, total of 9000 steps. The batch size is 64, chunk size is and the learning rate is 8e-5 with weight decay of 0.1.
E
LOSS FORMULATIONS
Pixel-level Mean Squared Error: In Table 1 LMSE denotes reconstruction objective that mea- sures the average squared discrepancy between the predicted output the ground-truth target, applicable to both visual and action modalitics. The loss is defined as
LMSE
Σ WEn
|
fa e))l?
(13)
where is the ground-trute -ruth target, L c) is the model prediction conditioned on c (e.g., instruction, proprioception), Ω the index set over which the discrepancy is computed, and u enumerates elements in Ω.
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Continuous Diffusion. In Table [ Liff-cont denotes conditional diffusion objective that leams to predict the injected noise for a noised sample at timestep (optionally conditioned on c), enabling iterative denoising back to the clean data. Formally,
Coiff-cont Ex, mo-a((a))) (o.))) all| co(xe, c)l 2t = a,Ju + oee, p {1,2),
(14)
where zo the clean continuous target, 2y its noised version at timestep t, is Gaussian noise, is an optional conditioning signal (e.g.. instruction, proprioception), a and σN are the noise schedule coefficients timestep
the diffusion timestep sampled from a predefined schedule, and E { denotes the norm used for the loss (typically 2 for L2 or = 1 for LI/Smooth-LI).. The model predicts and then used in reverse updates Ft-1 = Fo (2,, t, c) reconstruct Zo.
By default LDiff cont uses an L2 error for noise prediction. Some implementations (e.g., GR-1 (Wu al., (024), SEER (Tian et al.) 2025)) replace it with Smooth-I.1 (Huber) without altering the diffusion formulation. Formally, replace the 1.2 error with Smooth-L.1
1 |||
1r < B,
pa(r)
2
(15)
||r||a 2 B,
otherwise,
with r c co(2,, t, c) and 3 > 0 (default [3 = 1 unless specified). The diffusion objective becomes [rps(r)].
Discrete Diffusion. In Table Liiff-dicc denotes masked-toker prediction objective for discrete sequences over finite vocabulary (including a special [MASK]). ). Formally,
Fuasseedd c) {)]
Dpiff = EM
log pa (2.²
(16)
M
iEM
where M is the set of masked positions, = is the ground-truth token at position i, " is the visible context obtained by replacing positions in M with [MASK], and e an optional conditioning signal. The model outputs categorical distribution over the vocabulary and learns to recover the masked tokens from the visible context.
(MM)
Next-Token Prediction. Table I CNTP denotes the standard autoregressive objective (negative log-likelihood over the vocabulary) that maximizes the probability of the next token given its history:
LNTP E. log Po wee))]
(17)
where Jie the preceding context and x; is the next token. This loss encourages the model to assign high probability to the ground-trui truth continuation under causal (history-only) factorization.
F TRAINING DETAILS.
The training for the three virtual environment experiments and the real-world experiments was conducted on H100 GPUs. For the Calvin-ABCD dataset, we set the action chunk to 10 and trained for approximately 24 hours across 8 H100 GPUs. For the LIBERO dataset, we jointly trained on four tasks instead of training each task separately, with the action chunk set to 10, and the training lasted around 30 hours on 8 H100 GPUs. In the SimplerEnv environment, we used the Bridge dataset for training, with the action chunk set to 5, and trained for about 30 hours on 8 H100 GPUs. For the real-world experiments, we collected over 600 trajectories across tasks, designed with an action chunk of 8, and trained for around 8 hours on H100 GPUs.
G BASELINES
MCIL (Lynch & Sermanet 2020). MCIL introduces sngguage-coniitionee robotic manipulation with a single end-to-end visuomotor policy that learns perception from pixels, natural language un- derstanding, and multitask continuous control. A key contribution multicontext imitation learning,
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which enables training from largely unstructured and unlabeled demonstrations (no task or language la- bels), reducing language annotation to under I 1% of the dataset while improving language-conditggg conditiond performance. At test time, the policy follows dong-horizon free-form instructions in 3D tabletop environment using only text prompts. MCIL further combines any language-conditioned policy with large pretrained language models to handle many aut-of-distribution synonym instructions across multiple languages without collecting new demonstrations.
RT-1 (Brohan et al.) 2023). RT-1 is single Transformer visuomotor policy that encodes camera images, natural lang instructions, and motor commands into token sequences for real-time control. It is trained on over 130k demonstrations collected across 13 robots. RT-1 reports 97% success on over 700 instructions, improved generalization to novel tasks, distractors, and backgrounds. RT-1 can also execute long-horizon procedures within SayCan (Ahn al. 2022), with as many as 50 stages. RT-1 can also absorb heterogeneous data from simulation and other robot morphologies while retaining performance on the original tasks.
(Li et al., 2024a). RoboFlamingo builds on the open-source VLM OpenFlamingo to form a lasnuue manipulation framework. It decouples visual-language understanding and decision making: the pretrained VLM is used for per-step comprehension of images and in- structions, while an explicit policy head models history. Then the policy is fine-tuned solely on Longuage-conditionn
manipulation datasets via imitation learning. This design lets small amount of demonstrations adapt the model to downstream tasks, supports open-loop control. On the CALVIN benchmark, it outperforms prior baselines (Brohan al. 2023) and demonstrates zero-shot general- ization to new settings.
GR-1 (wu et al., 2024). GR-1 a straightforward GPT-style transformer for multi-task, annuuage-ondittioned visual robot manipulation. It takes as input a language instruction, a se- quence of observation images, and robot states, and predicts both robot actions and future images end-to-end. The model is first pre-trained for video prediction on large-scale video dataset, then seamlessly fine-tuned on robot data. On the CALVIN benchmark, the paper reports improvements over contemporaneous baselines (e.g., success rate from 88.9% to 94.9%, average length from 3.06 to 4.21), strong zero-shot unseen-scene generalization (53.3% to 85.4%). Real-robot experiments further show performance gains and robustness to unseen scenes and objects. OpenVLA (Kim et al., 2025). Open a 7B-parameter open-source VLA trained on 970k real-world robot manipula- tion trajectories from Open-X Embodiment. It uses visually conditioned Llama-2 backbone and fuses pretrained features from DINOv2 and SigLIP, capturing visual cues at multiple granularities. The paper reports strong generalist manipulation results, outperforming the 55B RT-2-X by 16.5% absolute success across 29 tasks and multiple embodiments with roughly 7x fewer parameters.
Recon (Song et al., 2025c). ReconVLA is reconstructive VLA with implicit grounding paradigm that addresses the observation of dispersed visual attention in prior VLAs. takes current images, language instructions, and robot proprioception as inputs. By reconstructing gaze regions corresponding to target objects, the policy acquires fine-grained representations and allocates attention task-relevant regions, enabling precise manipulation. The authors also curate large-scale pretraining corpus (over 100k trajectories and 2M samples) from open-source robotic datasets to improve generalization in visual reconstruction. Experiments in long-horizon simulation and real-world settings report superior performance, directive attention visualizations, and generalization to unseen objects and scenes.
UP-VLA (Zhang et al. 2025a). UP-VLA revisits VLA pre-training by coupling multi-modal understanding with future visual prediction to retain both high-level semantics and low-level spatial cues. Concretely, it co-trains an autoregressive policy with flexible attention mask on heterogeneous datasets, using lasion-language understanding to align semantic features and video-style future prediction to capture fine- mne-grained visual patterns. UP-VIL VLA reports that it understands instructions, predicts future images, and plans actions within one framework, with gains across simulated and real-world manipulation; in particular, it improves CALVIN ABC-DD generalization by about 33% over prior methods and shows stronger success on tasks requiring precise spatial control, while maintaining competitive in-distribution multitask and real-unseen performance.
UniVLA (Wang et al., 2025). UniVLA learns cross-embodiment VLA policies by planning in a unified, task-centric latent action space extracted from videos. The approach derives latent actions in a DINO feature space to separate task-relevant dynamics from irrelevant visual changes, enabling the
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Published as a conference paper at ICLR 2026
use of heterogeneous, web-scale data (including unlabeled human videos) without action labels. A lightweight decoder (about 10.8M parameters) translates latent actions into executable trajectories, supporting efficient adaptation with limited downstream data. The paper reports strong results across manipulation, navigation, and real-robot evaluations, including improvements over OpenVL.A (Kim) et al. 2025) with a fraction of the compute (1/20 pretraining) and data (1/10 downstream), as well as gains on LIBERO (+18,5%), navigation (+29.6%), and real-world deployments (+3).7%). Performance scales with data diversity and remains robust under embodiment and viewpoint shifts.
Octo (Octo Team et al. 2024). OCTO is an open-source, transformer-based generalist robot policy pretrained on 800k trajectories from the Open X-Embodiment dataset. It maps multimodal input tokens (observations and tasks) to action tokens. supporting instruction via language commands or goal images and handling diverse camera configurations and robot platforms. The model can be adapted to new sensory inputs, action spaces, or morphologies by adding lightweight adapters and fine-tuning on small target dataset within hours on consumer GPUs. OCTO employs a diffusion action head to model expressive action distributions. Experiments across 9 robots and 4 institutions show strong out-of-the-box multi-robot control for single- and dual-arm manipulation and effective initialization for downstream fine-tuning.
SpatialVLA (Qu et al., 2025). SpatialVLA targets 3D spatial understanding for generalist robot policies by aligning spatial representations of observations and actions. It introduces Egocentric 3D (Fgo3D) Position Encoding to inject 3D context into visual features in the camera frame (avoid- ing per-robot calibration), and Adaptive Action Grids to discretize continuous robot actions into data-driven spatial grids, learning spatial action tokens that transfer across embodiments. Pretrained on 1.1M real-world robot episodes, Spatial is applied zero-shot numerous tasks and shows strong in-domain multi-task generalization and long-horizon trajectory inference in both simulation and real robots.
VLT-VLA (Zhao et al. 2025). CoT-VLA introduces visual ofain-of-thoughee for robotic control by first generating subgoal images as explicit intermediate reasoning steps and then conditioning actions on the current observation and the generated subgoal. The system is built on unified multimodal backbone, trained on Open X-Embodiment and action-less video datasets, and fine-tuned on downstream task demonstrations. A hybrid attention design uses causal next-token prediction for text and image generation and full attention to predict action dimensions jointly. The policy further employs action chunking to output short action sequences per step. Experiments in simulation and the real world show that visual chain-of of-thought improves policy performance, with reported gains over prior VLAs (e.g., + +17% real-world, +6% simulation), and strong results across multiple robot platforms and tasks.
TO-AAST (Pertsch et al. 2025). TO-FAST proposes frequency-space action tokenization scheme that compresses robot actions with a discrete cosine transform followed by byte-pair encoding, reducing inter-token correlation and enabling next-token prediction training of autoregressive VLAs on high-frequency and dexterous data. It improves markedly over per-dimension binning and learned vQ tokenizers in simulation and real-robot studies, and makes large-scale training on DROID feasible with cero-shot evaluation via language prompting. Building on this, TO-FAST is universal action tokenizer trained on IM real trajectories across embodiments, action spaces, and control rates, providing an off-the-shelf tokenizer for VLA training.
WorldVLA (Cen et al., 2025). WorldVLA is an autoregressive action-world model that unifies image, text and action understanding and generation within single LLM framework. It encodes images, language, and actions with separate tokenizers that share one vocabulary, so the world module learns environment dynamics by predicting future visuals from actions, while the action module uses visual tokens to generate subsequent actions actions-yyelding mutual enhancement between world modeling and control. The authors observe performance drops when generating multiple actions autoregressively due to error propagation and limited action generalization; they address this with an action-attentio masking strategy that selectively masks prior actions when predicting the current one, improving chunked action generation. On LIBERO, WorldVLA improves grasp success by about 4% over an action-only backbone, reduces FVD for video generation by about 10% over a vanilla world model.
FlowVLA (Zhong et al. 2025). Flow instantiates a Visual Chain of Thought for world modeling by decomposing next-frame prediction into a motion- -hhhaaappee process, i.e., V, +
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Discrete Difussion
+=
Continuous Difussion
Figure 8: Qualitative Comparison of discrete diffusion and continuous diffusion.
u++1 ft U1+1 with fa an intermediate optical-flow target. By first committing to motion, the model learns disentangled dynamics that yield more coherent visual forecasts and more efficient policy learning. Practically, optical flows are encoded as RGB-like images and tokenized with the same VQ tokenizer as camera frames, sequence motion and appearance tokens under shared vocabulary. Experiments on challenging manipulation benchmarks report state-of-the-ar performance with substantially improved sample efficiency, supporting the claim that explicit motion reasoning better bridges pretraining and policy fine-tuning.
single autoregressive Transformer process interleaved
DreamVLA (Zhang et al. 2025b). DreamVLA recasts VLA as a perception-prediction-aatii framework by forecasting compact "world knowledge" support inverse dynamies. Instead of full frame prediction, it predicts targeted cues dynamic regions (via optical flow), depth maps, and high-level semantic features -to provide concise yet informative guidance for action planning. A block-wise structured attention masks cross-type interactions to prevent leakage and keep represen- tations disentangled, and diffusion-based transformer decodes actions from shared latents while separating action factors from non-action features. Experiments show strong results in simulation and the real world, including 4.44 average task length on CALVIN ABC-D and 76.7% real-robot success. Ablations indicate dynamic-region forecasting contributes the largest gains, with depth and semantics offering smaller, complementary benefits.
F1 (Lv et al. 2025). Fl integrates conditond visual foresight into the perception actionn loop and frames control as foresight-guiie guided inverse dynamics. The model adopts Mixture-of- Transformer with three experts for understanding, foresight generation, and action execution. A progressive attention scheme regulates information flow, and the generation expert uses a next-scale prediction mechanism to produce explicit planning targets. Training follows a three-stage recipe: Stage I aligns the generation expert with a pretrained understanding expert; Stage II pretrains the full model on large-scale public robot datasets; Stage III post-trains on task-specific data for embodiment adaptation and fine- fine-grained skills. Experiments across simulation and real-world platforms report consistent gains over reactive baselines, with improved robustness and generalization in dynamic and long-horizon tasks.
Mixuureoff-Tanssfemerr
Robo VLMs (L.iu et al. 2025). Robo provides a systematic study and a flexible framework for transferring foundation VLMs into VLAs, aiming to identify effective design choices. The work indicate that coninuoos-acction policy heads perform best and that model choice, architecture, and data strategy all materially affect performance and data efficiency. The study also examines when to leverage cross gross-embodiment data by contrasting pre- pre-training on Open-X - Embodiment, fine-tuning on target domains, and post-tran traitriiiing (pre-train then fine-tune). The resulting RoboVLMs achieve strong results simulation and real-robot settings and generalize to unseen distractors, backgrounds, objects, and novel skill descriptions.
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Published as a conference paper at ICLR 2026
H COMPARISON OF DISCRETE AND CONTINUOUS DIFFUSION
In discrete diffusion, the process involves transitioning between discrete states over time. For instance, in text generation, each diffusion step, a token might be randomly masked or replaced by another token. These transitions are controlled by a predefined schedule or transition matrix, which specifies the probability of moving between states (e.g.. retaining a token, replacing or masking it). A classic example is the "mask-prediee method used in language models, where tokens gradually emerge during sampling. Discrete models often rely on techniques like absorbing states (where tokens are masked) or uniform transitions between vocabulary items. In contrast, continuous diffusion models gradually add Gaussian noise to the data based on predefined noise schedule. For example, each step, the pixel values of an image may be slightly perturbed by noise until it becomes pure noise. During sampling, the model learns to reverse this process by predicting and subtracting the noise at each step. Figure 8 visualizes the differences in the denoising and noising processes between the two approaches.
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Reviews:
[{"review_id": "lZnnwzzrKB", "reviewer": "Reviewer_qxoz", "summary": "The paper presents a novel approach to build Vision-Language-Action(VLA) models. It tackles the current challenges that current methods often treat visual prediction and action prediction as two separate processes, which limits their mutual connection when training.\n\nThe authors propose the Unified Diffusion VLA (UD-VLA), a unified vision-language-action model that is built upon a Joint Discrete Denoising Diffusion Process (JD3P). In specific, it maps all modalities (texts, images, actions) into a shared discrete token space and then process with hybrid attention mask. \n\nThen, the authors design a two-stage training pipeline to activate the image generation capabilities by firstly post train the backbone for future-image prediction and then jointly train image and action. The paper reaches SOTA across CALVIN, LIBERO and SimplerEnv. More importantly, they claim four-time faster inference speed than normal autoregressive policies.", "questions": "•\tFigure 1 seems to be a little bit hard to follow especially for its training process and how JD3P works simply by looking at the figure.\n\n•\tIn Table 7, did the speed up represent the whole end-to-end process or simply the decoding process?\n\n•\tHow critical is the pretrained VLM working for the backbone? Can you have an additional ablation study, or some explanation clarify the performance is achieved compared to simply using baseline?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "•\tUnified Tokenization and Hybrid Attention: In this paper, all modalities are converted into a single sequence into a single sequence of discrete tokens instead of simply concatenation. Then the paper allows bidirectional attention within single modality but enforces causal attention across modalities. It’s a solid architecture design that naturally frames the task as utilizing futures images with actions but prevent leakage, improving the interpretability. Corresponding ablation study in Table 5,6 shows as a solid prof that hybrid attention is better than causal and simple bidirectional attention. In addition, future-image prediction provides more help than reconstructing the current-frame.\n\n•\tNovelty for JD3P: The idea of selectively reconstructs a subset of masked positions while leaving clean others hidden at each denoising step is interesting and therefore JD3P make sure future images and action tokens are generated within the same denoising step. It tackles the problem of weak connection in previous unified VLAs between visual generation and action prediction.\n\n•\tStrong Performance on various dataset: The paper shows SOTA performance on CALVIN, LIVERO, and SimplerEnv which proof its strong capabilities in long-horizon reasoning and execution, its strong generalization ability and its real-world transferability. \n\n•\tInference Efficiency: In Table 7, the paper demonstrates its 4 times faster compared to autoregressive methods and matin its performance advantage, which is significant.", "weaknesses": "•\tVisual Fidelity: Like the author also mentioned, the generated future images lack high visual fidelity, likely due to the VQ tokenization and lack of large-scale generative pretraining. While the author claims that it’s sufficient, it’s unclear how low fidelity will impact on specific tasks which required fine-grained visual details in real world settings.\n\n•\tSensitivity for hyperparameter: In Equation 8, the paper introduces β down-weights the visual tokens to avoid their dominance. It remains unclear for the difficulty to tune this without any training analysis. \n\n•\tPretrained VLM: The paper introduces its two-stage design pipeline when training. However, it utilized a recent powerful pretrained VLM which is effective but make it difficult to distinguish between the contribution of performance from JD3P from previous methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NSq65zUmfM", "reviewer": "Reviewer_8ozA", "summary": "This paper presents Unified Diffusion VvLA, a novel VLA method that tightly integrates understanding, generation, and action through a Joint Discrete Denoising Diffusion Process (JD3P). Unlike prior approaches that separate visual generation and action prediction, UD-VLA performs joint multimodal denoising in a unified tokenized space, enabling synchronous optimization of image and action generation. The authors propose a hybrid attention mechanism, a two-stage training pipeline, and inference-time optimizations to balance accuracy and efficiency. Extensive experiments on CALVIN, LIBERO, and SimplerEnv benchmarks, as well as real-world robot evaluations, demonstrate state-of-the-art (SOTA) performance and 4× faster inference compared to autoregressive baselines", "questions": "I don’t have additional questions.", "limitations": "", "rating": 8, "confidence": 2, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "Overall, I think that paper is a timely contribution that significantly advances the state of the art. The paper demonstrates clear innovation, rigorous experimentation, and practical relevance. \n\n- JD3P unifies multimodal generation and control within a single denoising trajectory, representing a conceptual and technical advance over modular or autoregressive VLAs.\n- UD-VLA achieves SOTA on multiple simulation and real-world benchmarks, consistently outperforming strong baselines such as UniVLA, F1, and DreamVLA, with notable efficiency gains (4× faster decoding).\n- The proposed attention structure effectively balances intra-modal richness with inter-modal causality, enabling actions to be grounded in predicted future observations.\n- The paper includes well-designed ablation studies that clarify the contributions of hybrid attention, future image generation, and joint denoising.\n- Demonstrating strong generalization to unseen environments and objects enhances the paper’s practical impact and credibility.\n- Despite the technical depth, the paper is well-structured, making complex ideas accessible to both robotics and multimodal learning audiences.", "weaknesses": "I do not think that paper has major weaknesses. Some minor ones:\n\n- While the appendix mentions that generated images lack fine-grained fidelity, the main text could better discuss potential failure cases or constraints (e.g., scalability to high-resolution images or multi-robot domains).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Pi5r5Yiukd", "reviewer": "Reviewer_jYyP", "summary": "In this work, the authors propose joint denoising of a subsequent visual frame and the action to take using discrete diffusion over a unified token space, with a causal attention mechanism such that the visual generation does not reference the action generation.  The authors report results over a variety of simulated settings as well as a real world setting.  Speed-up is done by KV-caching and fixing identifier token positions.", "questions": "In visual planning literature, IDM design $p(a \\mid s, s')$ is quite sensitive to finding an appropriate frame-skip/horizon $h$ such that $s'$ is $h$ timesteps away from $s$.  Sometimes a more aggressive future frame allows more useful information in predicting action $a$, especially if the rendering frequency is high (and there is lots of redundancy between $s$ and $s'$).  In this work, is $h$ always chosen to be 1 (is the subsequent frame generated always the temporally next frame)?  What choice for $h$ makes the method perform the best and under which environments/tasks?  Can you perform an ablation table over different choices of $h$?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The strengths of this paper lie in its engineering for fast inference (the authors report 4x faster than autoregressive techniques).  Furthermore, the hybrid attention mechanism is interesting, although it remains to be seen if it is indeed true that such a bias helps decision-making by adding thorough comparisons against visual planning techniques that also generate actions conditioned on future visual info (but future visual info generation is not conditioned on actions), as well as joint generation techniques that do not have this causal restriction (UVA, PAD).  This is elaborated further below.", "weaknesses": "The real-world setting was not elaborated upon properly; it is not obvious what objects are considered seen or unseen or what scenes are considered novel.  Appendix Section C does not offer any clarifying details beyond the information provided in Figure 3.  There are no visualizations for any of the tasks listed of \"stacking bowls, putting blocks (into a box) and flipping towers\"; rather the only visualization in Section B is an unrelated task of \"grasp the pink block\".  Simply grasping a block is quite different from pick-and-place.  Thorough details, visualizations, and demos of the tasks are necessary - particularly what novelty means.\n\nThis reviewer believes there is a large body of prior research that is relevant but missing from the submission.  The work seems to be premised off the idea that generating a future image first then decoding the intermediate action through inverse kinematics helps (Section 4.3, Lines 418-424).  This suggests that visual planning works, which explicitly generate future visual frames and then convert them into actions to execute through an inverse dynamics model, are quite relevant to compare against.  There is a rich body of work that investigates this approach [1,2,3,4] - which aligns with the authors' finding that generating a future image (or videos) before generating the action should help with decision-making.  Referencing them and even comparing against them; naturally, one may expect performance speed to be better for UD-VLA/JD3P with joint decoding; but success rate performance comparisons would be still interesting to report.\n\nSeparately, there are works that also seek to denoise subsequent images and actions jointly (UVA [5], PAD [6]).  It would be interesting to also compare against them, as they directly perform joint generation of action and images as in this work.  In this context, perhaps more emphasis can be put on the causal attention mechanism that enforces image generation not to condition on action generation even during the joint denoising process (which once again spiritually relates to visual planning approaches) - to differentiate from these joint vision-action denoising approaches.  \n\nTo summarize, the joint decoding part reminds readers of other joint image-action decoding techniques [5,6] but the fact that the authors chose to do so with causal attention reminds readers of visual planning techniques[1,2,3,4].  Combined, there is novelty in the proposed approach, but background works and comparisons should still be done for thoroughness.\n\n[1] Wang et al., Learning Robotic Manipulation through Visual Planning and Acting, RSS 2019.\n\n[2] Du et al., Learning Universal Policies via Text-Guided Video Generation, NeurIPS 2023.\n\n[3] Du et al., Video Language Planning, ICLR 2024.\n\n[4] Luo et al., Solving New Tasks by Adapting Internet Video Knowledge, ICLR 2025.\n\n[5] Li et al., Unified Video Action Model, RSS 2025.\n\n[6] Guo et al., Visual Policy Learning via Joint Denoising Process, NeurIPS 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "sOcHXGZmfc", "reviewer": "Reviewer_jgRu", "summary": "The paper proposes a unified vision-language-action model that jointly generates future images and predicts actions through a single synchronous denoising trajectory (JD3P), coupling understanding, generation, and acting in a shared tokenized space with hybrid attention. This joint diffusion approach eliminates reliance on external experts and avoids separating generation from control, delivering state-of-the-art performance on CALVIN, LIBERO, and SimplerEnv with 4× faster inference than autoregressive baselines.", "questions": "1. The single-step mask-prediction loss (Eq. 8) might limit temporal dependency modeling. Have multi-step noise-schedule variants been explored?\n2. A key question is whether JD3P’s synchronous denoising is dependent on the prediction tasks and backbone architecture. To disentangle this, I suggest: 1 Porting JD3P to an alternative backbone and repeating the training/evaluation to assess transferability; and\n2 Performing controlled comparisons under the same backbone and training protocol against fully autoregressive and partially autoregressive baselines (e.g., VQ-based image generation followed by action decoding).\nThese experiments would help determine whether improvements stem from the synchronous denoising mechanism per se, or from the image-prediction tasks/backbone, thereby isolating JD3P’s contribution.\n3. More details of setups, demos, and experiments in the real world are required.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper introduces a novel Joint Discrete Denoising Diffusion Process that enables the generation of image and action tokens.  This formulation effectively bridges the gap between perception and control, achieving intrinsic synergy across modalities.\n\nMethods: By constructing a unified tokenized multimodal space and employing a **hybrid attention mechanism** (bidirectional within modalities, causal across modalities), the model design is principled and well-grounded.\n\nExperiments: The experiments on simulation are comprehensive (across 3 simulation benchmarks).", "weaknesses": "1. **Limited theoretical depth:** The motivation for JD3P is intuitively explained, yet the heoretical justification of why joint denoising leads to superior cross-modal alignment remains shallow. A more formal analysis or deeper ablation would strengthen the claim.\n2. **Fairness of comparisons:** Some baselines may differ in model capacity or data scale, and these confounding factors are not fully controlled, potentially exaggerating UD-VLA’s relative gains (**more apple-to-apple ablation baselines are needed**).\n3. **Overly dense presentation:** The paper is lengthy and symbol-heavy. Some equations (e.g., βt, κt) appear abruptly, requiring readers to backtrack for context.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)