Unified Vision-Language-Action Model
Authors: Yuqi Wang
Xinghang Li
Wenxuan Wang
Junbo Zhang
Yingyan Li
Yuntao Chen
Xinlong Wang
Zhaoxiang Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: world model
robotics
vision-language-action model
Primary area: applications to robotics, autonomy, planning
TL;DR: a novel unified VLA model, world model enhance policy learning

Abstract: Vision-language-action models (VLAs) have garnered significant attention for their potential in advancing robotic manipulation.
However, previous approaches predominantly rely on the general comprehension capabilities of vision-language models (VLMs) to generate action signals, often overlooking the rich temporal and causal structure embedded in visual observations. In this paper, we present UniVLA, a unified and native multimodal VLA model that autoregressively models vision, language, and action signals as discrete token sequences. This tokenized formulation naturally supports flexible multimodal task learning, particularly from large-scale video data, and further demonstrates that generative vision supervision can significantly enhance visual understanding. By incorporating world modeling during post-training, UniVLA captures causal dynamics from videos, facilitating effective transfer to downstream policy learning—especially for long-horizon tasks. Our approach sets new state-of-the-art results across several widely used simulation benchmarks, including CALVIN, LIBERO, and Simplenv-Bridge, substantially outperforming prior methods. For example, UniVLA achieves 95.5% average success rate on LIBERO benchmark, surpassing π₀-FAST's 85.5%. We further demonstrate its broad applicability through experiments on real-world ALOHA manipulation tasks and autonomous driving scenarios.

Published as a conference paper at ICLR 2026
UNIFIED VISION-LANGUAGE-ACTION MODEL
Yuqi Wang'* Xinghang Li
Wenxuan Wang 1.2
Junbo Zhang?
Yingyan Li'
Yuntao Chen Xinlong Wang
Zhaoxiang Zhang 1E
I Institute of Automation, Chinese Academy of Sciences (CASIA)
2 Beijing Academy Artificial Intelligence (BAAI)
Tsinghua University
4 Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation
Project page: https::/ http:p:://rcbeetwgggggomm wwt.wb................. wtw..it........i.aaaaaaa
github. io
Unified Vision-Language-Acttnn Model
Action output
Multi-modal output
CALVIN Roonis
Language Model
Multi-modal Model
LIBERO
VIT
Static Image
Interleaved Video
BRIDGE
Unified Post-train Tasks
O lnstutte: booth
from driner
Inbruckox Put
May 22,
grope)]
Superiision
Superaaion
Action Superviaion
Perception Grounding
World Model
Policy Learning
Figure We present UniVLA, a unified iision-language-action model. Unlike prior VLA approaches that typically rely extra vision encoder to extract image features and generate only action outputs, UniVLA represents vision, language, and action as discrete tokens within unified autoregressive framework. This unified modeling paradigm enables multi-modal outputs and supports a wide range of tasks- as text-supervised perception grounding, rision-supervieed world modeling, and action-supervised policy learning-win -wittnn single architecture. The unified boken-based design further allows UniVLA to effectively leverage large-scale multimodal data, particularly video, for scalable and generalizable learning. UniVLA achieves new state-of-the-at results on CALVIN, LIBERO, and SamplerrEn-BBridgee significantly surpassing existing methods.
ABSTRACT
Vision-lagguage-attion models (VLAs) have garnered significant attention for their potential in advancing robotic manipulation. However, previous approaches predominantly rely on the general comprehension capabilities of languaee models (VLMs) to generate action signals, often overlooking the rich temporal and causal structure embedded in visual observations. In this paper, we present UniVLA, unified and native multimodal VL.A model that autoregressively models vision, language, and action signals as discrete token sequences. This tokenized formulation naturally supports flexible multimodal task learning, particularly from large-scale video data, and further demonstrates that generative vision supervision significantly enhance visual understanding. By incorporating world modeling during post- -taittraining UniVLA captures causal dynamics from vidcos, facilitating effective transfer to downstream policy
for hong-horizon tasks.
Our approach sets new state-of-the-arr results across several widely used simulation benchmarks, including CALVIN, LIBERO, and Simpleny-Bridge, substantially outperforming prior methods. For example, UniVLA achieves 95.5% average success rate on LIBERO benchmark, surpassing - 85.5%. We further
'Work done during internship BAAI Corresponding author

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Published as a conference paper at ICLR 2026
demonstrate its broad applicability through experiments real-world ALOHA manipulation tasks and autonomous driving scenarios.
INTRODUCTION
Developing agents capable of perceiving, reasoning, and acting in the physical world has long been a central objective of artificial intelligence. Recent advances in vision-language-action (VLA) models Brohan et al. (2023); Octo Model Team et al. (2024); Kim et al. (2024); Black et al. (2024), grounded in the powerful generalization capabilities of vision-language models (VLMss Peng et al. (2023); Jaech et al. (2024); Beyer et al. (2024); Wang et al. (2024a); Guo et al. (2025), have demonstrated impressive performance across a wide range of robotic manipulation tasks, and are increasingly being adapted to generalist humanoid robots Bjorck et al. (2025); Ding et al. (2025) that demand broader embodied intelligence. However, most existing VLA approaches Kim et al. (2024): Black al. (2024) follow language-centric paradigm: visual observations are first projected into a semantic space, and action policies are subsequently derived based on these representations. This late-fusion strategy, while beneficial for semantic understanding and generalization, limits the formation of deeply coupled cross moosll representations and impedes the learning of temporal and causal dependencies across the perception-acee language, and action be jointly modeled within a unified representation space to facilitate fighter cross-modal integration and more effective policy learming?
loop. This raises central question: Can vision,
While appealing in theory, unified modeling presents two key challenges. First, vision, language, and action are inherently heterogeneous modalities: vision comprises high-dimensional, continuous spatial signals; language conveys abstract, discrete semantics; and actions involve temporally ordered sequences with causal dependencies. Second, the perception-to-ace totatiionnn pipeline is inherently dynamic and causal, yet existing VLA models Brohan et al. (2023); Kim et al. (2024); Black et al. (2024) often adopt static, language-centric paradigms that merely learn the mapping from static image to action. These models fail to capture the dynamic nature of real-world interactions, thereby limiting their ability to leverage the rich temporal information from videos for training.
To address the above challenges, we introduce UniVLA, novel framework for unified vi- sion-language-action learning. As illustrated in Figure 1, we propose a unified framework that supports both multimodal and multi-task task learning. At the modality level, vision, language, and action signals are all transformed into discrete tokens and modeled using a shared vocabulary. This unified token representation allows for joint learning across modalities, fostering deeper cross-modal understanding and integration. Building upon the unified framework, we adopt an autoregressive, Markov thain-based sequence modeling approach, where observations and actions are interleaved. This structure naturally incorporates causal dependencies, enabling the model to reason over temporal dynamics rather than treating perception and action as isolated tasks. By integrating the world model paradigm during training, we leverage large-scale robotics videos for self-supervised learning, allow- ing the model to capture environment dynamics in a temporally consistent and causally grounded manner. Remarkably, we find that post-training with world models significantly enhances policy learning, particularly for long-horizon and out-of-distribution tasks.
Experiments across multiple simulation benchmarks, including CALVIN Mees al. (2022b), LIBERO Liu et al. (2023), and SimplerEnv Li et al. (2024d), demonstrating clear performance improvements over existing methods. Our model incorporates world model learning during post- training, enabling it to effectively capture visual dynamics from large-scale videos. This strategy significantly enhances both data efficiency and training efficiency in downstream policy learning. and allows for rapid adaptation to novel robotic tasks. Beyond policy learning, we demonstrate the model's 's multimodal output capabilities, including spatial reasoning and visual prediction, highlight- ing its versatility. Furthermore, we extend our approach to autonomous driving scenarios for broader applicability. These results underscore the potential of our unified VLA model as an alternative and promising direction for generalist embodied intelligence.
Our contributions are summarized as follows:
We propose UniVLA, the first unified vision- languagg-aaatttttti action (VLA) model that encodes vision, language, and action as discrete tokens within a shared vocabulary. jointly modeling them through autoregressive sequence learning. This approach offers novel architecture
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alternative to the existing VLA paradigm, facilitating more integrated cross-moda modeling and enabling large-scale video-based training.
Our unified sequence modeling framework supports a broad range of multimodal tasks. Through extensive experiments with various post-training strategies, we show that world models can effectively capture temporal dynamics from video data, substantially boost- ing performance and improving both data and training efficiency in downstream policy simulation benchmarks, real-world robotic platforms, and even driving domains, where world model post-training consistently benefits policy learning.
Our model achieves hate-of-theearr performance across multiple simulated benchmarks (CALVIN, LIBERO, and BaallerEE---riidgee and introduces an open-source VLA frame- work enabling large-scale video training. We further demonstrate strong multimodal capa- bilities, including spatial reasoning and video prediction, and validate effective transfer to real-world ALOHA platforms and autonomous driving scenarios, highlighting its promise for generalist embodied intelligence.
RELATED WORKS
2.1 VISIO--LANGUAGF--CCTIOO MODELS
Recent vision- Assionllanguage-cttinn (VLA) models have demonstrated strong task performance across diverse robots and tasks Brohan al. (2023); Vuong et al. (2023); Driess et al. (2023); Kim et al. (2024); Zhen et al. (2024); Cheang et al. (2024); Black et al. (2024); Zheng et al. (2024); Liu et al. (2025); Kim et al. (2025); Intelligence vision-language models (VLMs) to enhance understanding and generalization, further fine-tuned on large-scale robotic datasets for low-level control. Currently, VLA models can be categorized into two paradigms based on their output space: pure action prediction and visual- guided action prediction.
al. (2025). These models leverage pre-trained
Pure action prediction. Recent efforts have extended vision-language models (VLMs) to incorporate action modalities, enabling direct action prediction from visual and language inputs. A prominent example is RT-2Brohan al. (2023), which learns from both internet-scale and robotic data to generate discrete actions autoregressively, showcasing strong generalization and semantic grounding. Building upon this, RT-HBelkhale et al. (2024) introduces hierarchical actions facilitate data sharing across tasks. VL ViA et al. (2024) scales this paradigm with 7 7B-parameter open-source model trained on 970k real-world demonstrations spanning diverse datasets. To enhance spatial reasoning. SpatialVLAQu et al. (2025) integrates spatial representations into the action modeling process. Beyond architecture scaling, new action modeling techniques have also emerged. To Black et al. (2024) incorporates flow matching to improve action learning efficiency, while FAST Pertsch et al. (2025) introduces a unified frequency-don -omaii formulation for discretizing actions.
Visual-guided action prediction. These studies leverage the power of visual pretraining, typically based on a policy-as-video formulation, by predicting future visual signals and subsequently decoding them into actions. SuSIE Black et al. (2023) predicts key future frames and derives actions through inverse dynamics. UniPi Du et al. (2023) generates videos from text instructions, extracting actions from the frames. GR series Wu et al. (2024); Cheang et al. (2024); Li al. (2025a) leverages video pretraining for general policy learning. PAD Guo et al. (2024) uses diffusion models to simultaneously learn future images and actions. LAPA Ye et al. (2025) proposes to learn latent actions between images with VQ-VAE from action-free internet-scale videos. Track2Act Bharadhwaj et al. (2024) extracts point tracks from diverse web videos to guide the learning of interaction plans.
Both approaches have complementary strengths and limitations. The first, focused on action pre- diction, integrates naturally with Vision-LLanguage Models (VLMs) but lacks spatial reasoning and visual forecasting. The second, incorporating visual generation, requires separating generative and action prediction models, limiting unified multimodal learning. Our work bridges these paradigms by combining video-centric pretraining with VLM capabilities, resulting in native multimodal model with strong future potential.
Regarding comparisons with current VLA architectures such as To.s Intelligence et al. (2025). our framework prioritizes video-centric modeling during the pretraining stage. While tokenizing actions may introduce marginal trade-off in low-level control precision compared to continuous heads, this
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L
L5
Pre-train VL-Align
Text Tokens
Autoregressive Transformer
World Model
1.2
I.
Fine-tune
Action Tokens
Policy Learing
Task
Pick carrot
W
Causal Multimodal Sequence
Figure 2: Overview of the UniVLA framework. Our model unifies information from different modalities into discrete interleaved sequence, which is modeled using an autoregressive Transformer. To enable unified modeling, images are discretized using vector-quantized (VQ) encoders, while actions are transformed into the frequency domain and discretized via Discrete Cosine Transform (DCT) encoding. This causal multimodal sequence naturally preserves the temporal dynamics and causality required for real-world tasks. The model builds upon pretrained vision-lange sooonlangguag model and follows a two-stage training strategy: (1) post- teat-trriinng phase that adopts world-mode training on large-scale datasets without requiring actions, and (2) fine-tuning phase that interleaves actions into the sequence, enabling policy learning on downstream tasks.
unified architecture offers significant advantages in pretraining scalability and multimodal alignment. Furthermore, this foundation is inherently extensible, accommodating specialized action experts during tinettunin for scenarios requiring high- digh-precision actuation.
2.2 WORLD MODELS FOR ROBOTICS
World models Ha Schmidhuber (2018); Hafner et al. (2019a); LeCun (2022) have gained widespread attention for their ability to capture and reason about the dynamics of the physical world. They have emerged as a cornerstone in range of domains, including interactive video generation Bruce al. (2024); Che et al. (2024), autonomous driving Hu et al. (2023a); Wang et al. (2024d;b); Gao et al. (2024), and robotics Du et al. (2023); Wu et al. (2023); Yang et al. (2023). Recent advances in robotics increasingly focus on general-purpose controllable video generation to simulate realistic and diverse robot-environment interactions. Visual Foresight Finn & Levine (2017) leverages action-conditioned video prediction with model-predictive control, enabling robots to plan manipulation tasks by forecasting future visual observations. UniSim Yang et al. (2023) builds a "universal simulator" trained on diverse visual datasets, capable of visualizing the effeets of both high-level instructions (e.g., "open the drawer") and low-level controls in novel scenes. RoboDreamer Zhou al. (2024) learns compositional world model by factorizing video generation, facilitating the synthesis of novel action sequences. DREMA Barcellona et al. (2024) replicates scene dynamics and structure by integrating Gaussian Splatting with physics simulation. VLP Du al. (2024) enables long-horizon visual planning by combining text-to-vides generation with vision- language models as heuristic evaluators. DayDreamer Wu et al. (2023) extends Dreamer Hafner et al. (2019b) to real-world robotic platforms, while UVA Li al. (2025b) proposes joint video-action latent space to decouple video and action generation, achieving high accuracy and efficiency in policy inference. Aorld Gao et al. (2025) extracts latent actions from videos in self-supervised manner and builds an autoregressive world model conditioned on these latent actions.
UNIFIED VISION-LANGUAGE-AACIIOO MODEL
In this section, present the design of UniVLA, illustrated in Figure 2. Unlike previous VLA models Kim et al. (2024); Black et al. (2024) that rely on ViT Dosovitskiy et al. (2021) for image encoding, approach adopts encoder-free free architecture, converting all modalities into discrete tokens and learning them autoregressively. The overall design simple yet effective, demonstrating strong scalability.
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Our unified paradigm has two key aspects: first, it unifies the learning of multiple modalities, integrating various modality tokens into a shared representation space and employing a transformer for autoregressive learning; Second, it unifies sequence modeling across tasks through the natural interleaving of modalities, facilitating the seamless combination of tasks such as video generation, visual grounding, and action learning. In the following sections, will introduce the method from the perspectives of Unified Multimodal Model and Unified Multimodal Sequence Modeling.
3.1 UNIFIED MULTIMODAL MODEL
As illustrated in Figure 2, our method unifies language, vision, and action modalities by converting each into discrete tokens and concatenating them into a single multimodal sequence T.. Specifically, Tr, Tea, and Ta denote the discrete token sequences for language, vision, and action, respectively, all drawn from a shared vocabulary. The superscript indicates the temporal step, with tokens interleaved across modalities to preserve temporal alignment.
For example, in the robotic manipulation task, a textual instruction is provided only at the beginning, followed by naturally interleaved sequence of visual observations and actions. The language and vision tokenizers adopt the same design as Emu3 Wang et al. (2024c); visual observations are discretized using VQ tokenizer Zheng et al. (2022), while actions encoded using FAST Pertsch et al. (2025). To clearly demarcate modality boundaries, we employ special tokens- (begin of image), eoi (end of image), boa (begin of action), and eoa (end of action) to encapsulate image and action tokens, respectively.
Action Modeling We follow FAST Pertsch et al. (2025) and apply the Discrete Cosine Transform (DCT) convert continuous action sequences into discrete action tokens. Specifically, given action sequence within a time window, we define La at a given time step as a sequence of action tokens [T....., T- where each action a, is a d-dimensional vector. The FAST action tokenizer encodes A into a discrete token sequence .T,,..., TR), with tokens drawn from vocabulary of size [V]. Similar to natural language processing, action sequences can vary in token length, resulting in a variable-length (n) discrete representation.
The action sequence AI. {ai, {ai,a2.-.. a2, .......... ax spans window of size H,
Training Objective Since all modality signals are transformed into discrete tokens, the training objective is simplified to a standard next-token prediction task using cross-entropy loss. To accommo- date different task formats, we selectively include specific tokens in the loss computation, ensuring compatibility and flexibility across diverse tasks.
3.2 UNIFIED MULTIMODAL SEQUENCE MODELING
As shown in Figure 2, our multimodal sequence representation naturally captures the temporal dynamies and causal structure inherent task execution. The embodied planning problem can be formulated as a Markov Decision Process (MDP), a general mathematical framework for decision- making in partially stochastic environments. For example, instruction and current observation inform the action; this action alters the environment, leading to new observation that subsequently guides the next action. Building on this interleaved Markovian formulation, we unify variety of tasks within shared sequence modeling framework, and present the task-specific modeling strategies in the following.
the task of picking a carrot, the
World Model (Post-train) Within the MDP framework, a world model aims to learn the dynamics of the environment by modeling the transition function P(s a.). The learned world model enables agents to simulate future trajectories, plan actions, and reason about consequences without direct interaction with the environment. Specifically, in the context of robotic tasks, we treat the language instructiom as a general form of action. Given the current observation L! and the instruction L!, the world model need to predict future visual content. In this setting, use the loss computed solely from the vision tokens as the supervisory signal, enabling the model to generate visual predictions conditioned the given instruction and observed state. Sequence S, formulation is as follows:
= 03 L2 D
(I)
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Policy Learning (Fine-tune) Policy learning enables the agent to determine optimal actions based on both current observations and prior states, thereby effectively guiding task execution. In this setting, employ loss function computed solely from the action tokens. The sequence S, representing the interactions over time is formulated as:
S. = (L) L. 1 L2 La.....
L Li
As illustrated in Figure 2, in this interleaved format, we adopt two-stage training paradigm for robotic tasks. The model is initialized with a vision-language (VL) aligned checkpoint, endowing it with basic vision-language capabilities. The post-training stage leverages a world model objective to capture video dynamics, treating world modeling as general visual learning task. Building upon the learned world model, the fine-tuning stage focuses on action learning refine mis-sspeifiic behaviors. We observe that incorporating the world model significantly enhances the efficiency and effectiveness of policy learning.
EXPERIMENTS
4.1 DATASET
CALVIN. CALVIN Mees et al. (2022b) is simulated benchmark tailored for evaluating long- horizon, language--onddtionee robotic manipulation. It comprises four simulated environments (A, B, C, and D), each containing demonstration trajectories collected via human teleoperation. The benchmark encompasses 34 distinct manipulation tasks with a total 1,000 unique language instructions. Performance is measured by the average number of successfully completed sub-tasks within a sequence. Standard evaluation protocols include the ABC-D and ABCD-D settings, which test model's ability to generalize to unseen environments and compositions of long-horizee tasks.
LIBERO. The LIBERO benchmark Liu et al. (2023) is comprehensive suite for lifelong robotic manipulation, comprising four task suites with 10 tasks and 50 human demonstrations each. These suites are designed evaluate different generalization abilities: LIBERO-Spatte Spatial tests spatial rea- soning by varying layouts with fixed objects: LIBERO-Object assesses object-level generalization with varying objects in a fixed scene: LIBERO-Goal targets goal-conditioned behavior by varying task goals; and LIBERO- Long (LIBERO- 10) features long- Long-horizon, compositional tasks with diverse objects, layouts, and goals, challenging temporal and compositional reasoning.
SimplerEnv. SimplerEnv Li et al. (2024d) serves simulation benchmark designed to evaluate the transferability and generalization capabilities of models trained on real-world video data. It incorporates diverse manipulation tasks on WidowX and Google Robot platforms with variations in lighting. object textures, colors, and camera viewpoints.
4.2 IMPLEMENTATION DETAILS
The model a purely autoregressive Transformer with 8.5B parameters, identical to Emu3 Wang et al. (2024c). Images tokenized via VQ-based encoder with 8x spatial compression. Actions are encoded as frame-tor comme-to-freee to-frame differences, normalized by the 1st/99th percentiles, and tokenized with FAST Pertsch et al. (2025), whose 1024-token vocabulary replaces the last 1024 IDs of the language tokenizer.
Post-training Stage. the post-training stage, we leverage large-scale robot-centric video datasets to study the effects of various post-training strategies on downstream policy learning. The model is initialized with pre-trained weights from the first stage of Emu3 Wang et al. (2024c). We curate a total of 622K videos from existing robotics datasets (details provided in the appendix), and identify the world model as the most effective post-training approach. During training, supervision is applied solely the vision tokens. The model is trained for 30K steps with batch size of 64.
Fine-tuning Stage. During fine-tuning, the model is initialized with weights from the post-training stage and trained using a two-frame interleaved vision-actto sequence with an action chunk size of 10. A cosine annealing learning rate schedule is applied, starting at 8 x 10 and the loss is computed solely over action tokens. For the CALVIN benchmark, RGB observations from both third-person (200 200) and wrist-view (80 x 80) cameras are used. Training is conducted on A A100

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GPUs with batch size of 192 for 8k steps. For the LIBERO benchmark, RGB images (both at 200 x 200) are used to train a unified model with a batch size of 192 for 8k steps. A single model is evaluated across four task suites. For the SimplerEny benchmark, single-view RGB observations are used with input resized to 256 x 256. Training is conducted on the Bridge-Wing WidowX setup using batch size of 128 for 20k steps, with an action chunk size of 5.
and wrist-view
Inference. Our method adopts an interleaved vision-actio action training scheme, requiring supervision from future frames only during training. During inference, the model generates only action tokens without predicting future frames, conditioning on the current observed images. The action generation stops once the model predicts the eoa (end of action) token.
Additional implementation details on the post-training strategy, real-robot fine-tuning procedures, and autonomous driving experiments are provided in the appendix.
Table 1: Long-horizon robotic manipulation evaluation on the CALVIN benchmark. denotes the use of video inputs.
Method
Task
Tasks Completed Row
Avg. Len
MCIL Lynch & Sermanet (2020)
ABCD-D
0.373
0.027
0.002
0.000
0.000
0.40
RT-1 Brohan et al. (2022)
ABCD-D
0.844
0.617
0.438
0.323
0.227
2.45
Robo-Flamingo Li et al. (2024c)
ABCD->
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
UP-VLA Zhang (2025)
ABCD-D
0.962
0.921
0.879
0,842
0.812
4.42
RoboVLMs* et al. (2024b)
ABCD-D
0.967
0.930
0.899
0.865
0.826
4.49
UniVLA
ABCD-D
0.985
0.961
0.931
0.899
0.851
4.63
MCI. Lynch & Sermanet (2020)
ABC-D
0.304
0.013
0.002
0.000
0.000
0.31
Robo-Flamingo ct al. (2024c)
ABC-+D
0.824
0.619
0.466
0,331
0.235
2.47
SuSIE Black et al. (2023)
ABC- DD
0.870
0.690
0,490
0,380
0.260
2.69
GR-1* Wu et (2024)
ABC-+D
0.854
0.712
0.596
0.497
0.401
3.06
MoDE Reuss et (2024)
ABC-D
0.962
0.889
0.811
0.718
0.635
4.01
UP-VLA Zhang al. (2025)
ABC-
0.928
0.865
0.815
0.769
0.699
4.08
RoboVLMs' Li ct al. (2024b)
ABC+D
0.980
0.936
0.854
0.778
0.704
4.25
Seer-Large Tian al. (2024)
ABC-
0.963
0.916
0.861
0.803
0.740
4.28
UniVLA"
ABC +D
0.989
0.948
0.890
0.828
0.751
4.41
4.3 MAIN RESULTS
In this section, we evaluate our method on three simulation benchmarks: CALVIN (long-horizon tasks), LIBERO (diverse generalization), and SimplerEnv (real-to-sim manipulation). Our approach consistently achieves state-of offttf--tatttt performance across all settings.
CALVIN Simulation Evaluation. Table I presents the experimental results in the CALVIN benchmark. Our method achieves the highest performance both the ABC-D and ABCD+DD tasks, significantly outperforming previous approaches and demonstrating strong capabilities in multi-task learning and Long-horizon planning.
LIBERO Simulation Evaluation. Following Zhao et al. (2025), report the average success rate over 500 episodes for each task suite (Spatial, Object, Goal, Long). As shown in Table 2. UniVLA achieves the best overall performance across all LIBERO benchmark suites, with particularly significant gains on long-horizon tasks tasks-raising the previous best from 69.0% to 94.0%. Compared to To Pertsch et al. (2025), method demonstrates superior performance long- horizon tasks.
Table 2: Comparison of different methods on the LIBERO benchmark.
Method
SPATIAL
OBJECT
GOAL
LONG
Average
DP Chi et al. (2023)
78.3%
92.5%
68.3%
50.5%
72.4%
Octo Team al. (2024)
78.9%
85.7%
84.6%
51.1%
75.1%
OpenVI.A Kim al. (2024)
84.9%
88.4%
79.2%.
53.7%
76.5%
SpatialVLA Qu et al. (2025)
88.2%
89.9%
78.6%
55.5%
78.1%
CoT-VLA Zhao et (2025)
87.5%
91.6%
87.6%
69.0%
81.1%
F.-FAST Pertsch et al. (2025)
96.4%
96.8%
88.6%
60.2%
85.5%
UnIVLA (single-frame)
97.0%
99.0% %
92.6%
90.8%
94.8%
95.4%
98.8%
93.6% %
94.0%
95.5%

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SimplerEnv Simulation Evaluation. Table summarizes the performance actoss various manipu- lation policies on the Bridge- WidowX setup. Our approach demonstrates a significant improvement over prior methods. raising the average success rate from 42.7% to 69.8%. In particular, it shows marked improvements on previously difficult tasks, including stack block, put carrot and put spoon.
Table 3: Evaluation on SimplerEnv-Widowy across various manipulation tasks.
Model
Put Spoon Towel
Put Carrol Plate
Stack Greem
Vellow Black
Put Fazplant in Yellow Baoket
Overall
Grasp
Suuces
Grasp
Succos
Suaress
Grasp
Sucucsa
Succes
KF X Brehan et (2023)
16.7%
0.0%
20.3%5
4.2%
8.3%
0.01
0,0%%
0.815
1.1%
Octo Octo Model Tean ot (2024)
34.7%
12.5%
52.85%
5.3%
31.9%
0.1%
66.79%
43.1%
16.0%
Octo Small Osto Model Team et (2004)
77.8%
47.2%
27.4%
9.7%
40.35
4.2%
$7.55%
$6.95
29.35
OpesVLA Kim al. (2024)
4.1%
0.0%
33.3%
0,0%
12.5%
0.01
9,3%
4,1%
1,0%
RoboVLMs La et (3024b)
70.8%
45.8%
33.3%
20.8%
$4.2%
4,21
91.7%
79.2
37.5%
SputiaVLA Qu (3025)
30.8%
16.7%
29.29%
25 25.0%
62.5%
20.256
100%
100%
42.7%
CogACT Li (20144)
71.1%
50.89
15.0K6
67.5%
51 1.33%
UniVLA
X3.3%
83.3%
74.0%
66.7%
85.8%
33.3%
100.0%
95.85
68.8%
4.4 IN-DEPTH ANALYSIS
This section presents an in-depth analysis of our unified framework, deriving critical design insights for future VLA models. We primarily focus the pivotal role of the world model, illustrating how its integration as a post-training strategy fundamentally advances downstream policy learning capabilities (Table and accelerates training efficiency (Table 5). We then turn ablation analysis of the module design. We find that even without post-training stage, incorporating visual prediction loss (Table 6a) and historical context (Table 6b) still contributes positively to policy learning.
Effectiveness of World Model Post- Dost-Training. Table 4 investigates the effects of different post- training strategies on downstream policy learning across various simulation benchmarks. Our results reveal that action-only post training suffers from limited transferability, primarily due to the non-uniformity of action spaces across diverse robot datasets fatasets-where whheee variations in embodiments, control frequencies, and normalization result in heterogeneous distributions. In contrast, most vision-based post-training approaches significantly enhance policy learning and demonstrate superior generalization without relying explicit action labels, thereby highlighting the crucial role of visual learning in transferability. Among these, the world model post-training approach yields the most substantial gains, enhancing both generalization and long-horizon planning capabilities. A comparison with text-to-image (T2I) training emphasizes the importance of modeling temporal dynamics in video data, while video-only training reveals the importance of textual guidance for state transitions. Notably, this world model requires no action annotations, enabling scalable learning from large-scale video data and offering promising direction for future VLA research.
Data and Training Efficiency for Fine-tuning. Table 5 shows that post-training substantially enhances downstream policy learning efficiency. On the CALVIN benchmark (Table 5a), our method achieves higher success rates using only 10% of the fine-tuning data, outperforming prior approaches such as GR-1 Wu et al. (2024) and RLbs Li et al. (2024b). In addition, Table 5b highlights improved training efficiency, as the model rapidly converges with fewer fine-tuning iterations. The Simpler-Fan results further demonstrate the effectiveness of world-modelibasede post-training for efficient policy adaptation across diverse robotic setups. While similar effects observed in latent- action methods Ye et al. (2025); Chen et al. (2024b); Gao et al. (2025), our world model offers a simpler paradigm without latent actions, achieving better transferability.
Effectiveness of Visual Prediction. While post-training proves effective, it is also crucial that the model demonstrates strong performance without relying on it. As shown in Table 6a, our findings
Table 4: Effectiveness of World Model Post- post-Training. We compare different post-training strategies by fine-tuning only with action prediction on the downstream benchmarks.
Post-training Stage Sequence
Generalization
Long-horizon CALVIN
Strategy
Supervision
LIBERO
SimplerEnn-WidowX
LIBERO-Long
48.5
0.0
17.4
1.46
action prediction
T,IAA T.I 1
action
43.9 (-4.6)
0.0
10.6 (-6.8)
0.52(-0.94)
text-to-image
vision
69.8 (+21.3)
6,3 (+6.3)
55.8 (+38.4)
3,79 (42,33)
video prediction
vision
78.9 (+30.4)
17.7 (+17.7)
80.8 (+63.4)
3,59 (42.13)
world model
T.h....
94.2 (+45.7)
64.6 (+64.6)
89.2 (+71.8)
4.61 (43.15)
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Published as a conference paper at ICLR 2026
Table Post-training enables data-efficient and training-eflicient downstream policy learning.
(a) Data efficiency comparison.
(b) Training efficiency comparison.
Method
Data
CALVIN
Fast convergenee (CALVIN)
Training Iters
2k
4k
8k
RT-1 Brohan (2022)
10%
0.34
MT-R3M Nair al. (2022)
10%
0.61
w/o train
0.37
0.82
1.46
HULC Mees al. (2022a)
10%
1.11
w/ post-train
4,21
4.56
4.61
GR-1 Wu et al. (2024)
10%
2.00
Fast adaptation SimplerEnx-Bridgee
RoboVLMS Li et al. (2024b)
10%
2.52
Method
Batch size
Iters
Success
UniVLA (w/ post-train)
RoboVLMs Li al. (2024b)
128
50k
37.5
UniVLA
128
12k
64.6
UniVLA
10%
3.19
Table 6: Ablation study the visual prediction and historical context in policy learning.
(a) Effectiveness of visual prediction.
(b) Effectiveness of history context.
Post-train
Visual prediction
CALVIN
LIBERO
Observations
Avg. Len 1
4.61
94.2
History Window
Current History
4.42
88.7
0
1+0
4.26
1.46
48.5
4.61
10
1+2
4.43
20
1+2
4.47
indicate that, without post-training, fine-tuning with visual loss ceteevisioo-eeverraaee the autoregressive nature of the model moodel-ntturally integrates world model learning into the policy learning process. This approach leads significant improvement in the model's performance.
Effectiveness of History Context. History context- past observations and ac- pronsderovids valuable guidance for robot planning. In this section, we investigate the appropriate length of the history window during the fine-tuning stage. As shown Table 6b, our ablation study on the CALVIN benchmark examines the impact of varying history window lengths. Incorporating a history window significantly improves performance (from 4.26 to 4.61). However, extending the window beyond certain length yields diminishing returns, suggesting that recent observations carry the most predictive value, consistent with the Markov property in sequential planning.
4.5 BROADER APPLICATIONS
Real-world ALOHA Tasks. We further validate the effectiveness of approach the real-world ALOHA platform through two tasks: pouring water and folding clothes. Our model relies exclusively on visual observations, without access any state information.
As shown in Table 7, we decompose the pouring task into three sequential subtasks: right-hand grasping the bottle, left-hand grasping the cup, and pouring. UniVLA demonstrates competitive performance against Black et al. (2024) in real-world ALOHA tasks. While To achieves marginally higher success rate the grasping stage, both methods exhibit comparable performance in the pouring stage. All results are averaged over 8 trials. Notably, we observe that precise pouring remains challenge for both methods. Interestingly, we observe that world model post-training real-world experiments. Additional details and
yields substantial performance improvement demonstration videos are provided in the appendix.
Table 7: Comparison across real-world ALOHA manipulation tasks.
Method
hand Grasp Bottle
Left-hand Grasp Cup
Pour Water
Overall
To Black al. (2024)
87.5
75.0
37.5
37.5
UniVLA (w/o world model) UniVLA (w/ world model)
12.5
0.0
0.0
0.0
87.5
62.5
37.5
37.5
As shown in Figure 3, despite the precision loss from discrete tokens, our method still able to perform fine-grained tasks such folding clothes.

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Published as a conference paper at ICLR 2026
Figure 3: Visualizations of the folding clothes task performed by our method.
End-to-end Learning for Autonomous Driving. To further explore the potential of our method, we perform a preliminary transfer to the autonomous driving domain by finetuning the model on the NAVSIM benchmark. Notably, method is pure autoregressive, token-based framework, modeling the driving task as causal sequence prediction over discretized multimodal tokens. Despite using only front-view camera inputs-withour relying BEV representations multi-sensor fusion-our model achieves powerful performance on the NAVSIM test set. Notably, world model post-training is also effective in the driving domain and can lead to significant performance improvements. These results highlight the strong potential of our method for broader real-world applications.
Table 8: Broader applications of UniVLA for end-to-end autonomous driving on the NAVSIM.
MC: Multi Camera. L: LiDAR. FC: Front Camera.
Method
Model
Input
NC1
DAC1
EPI
TTC1
CT
PDMS
Human
100.0
100.0
87.5
100.0
Egg Status MLP
Ego State
93.0
77.3
62.8
83.6
100.0
65.6
VADv2 Chen (2024s)
BEV-based
MC
UniAD Hu al. (2023b)
BEV-based
MC
97.8
91.9
78.8
92.9
100.0
83.4
Transfuser Chita (2022)
BEV. based
MC&I.
97.7
92.8
79.2
92.8
100.0
84.0
UniVLA
Auto-regree regressive
FC
96.9
91.1
76.8
91.7
96.7
81.7
world post-train)
Auto-regressive
CONCLUSION
In this paper, we present UniVLA, a unified framework for dision-langueee -cttnnggeeciinnnn action modeling that bridges heterogeneous modalities through shared token space and models them autoregressively, The proposed unified design facilitates deeper cross-modal integration and inherently supports flexible multimodal tasks. By leveraging world model trained capture dynamics and causality from videos. we observe significant improvements in downstream policy learning, both in terms of performance and efficiency. Extensive simulation experiments further demonstrate the model's strong generalization ability, efficient policy learning, and broad applicability across diverse domains. These findings highlight the great potential of our method as a new paradigm for vision-languagc-action modeling.
Limitations and Future Work Our exploration of post-training scalability is currently limited by compute availability, yet early indicators show promise for scaling to extensive video datasets. Al- though our unified framework excels in cross-mis modal learning, further research is needed to seamlessly integrate reinforcement learning for robust policy adaptation. Regarding real-word ceal-world deployment, have validated the feasibility of our approach on tasks such as dual-arm pouring and cloth folding; however, generalizing more complex, open-ended manipulation tasks remains open challenge.
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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
We would like thank BAAI for providing the data and computing resources. This work was supported in part by the National Key R&D Program of China (No. 2022ZD0116500) the National Natural Science Foundation of China (No. U21B2042, No. 62320106010).
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16


Reviews:
[{"review_id": "1OqzaWpXDS", "reviewer": "Reviewer_U4v1", "summary": "This paper proposes a unified Vision-Language-Action (VLA) model that discretizes all modality data into tokens. Unlike previous VLAs that predict actions solely from current observations, this work predicts the next actions conditioned on interleaved visual-action histories. Furthermore, the paper introduces a world-model post-training strategy to effectively transfer knowledge from pretrained multimodal models to downstream robotic tasks. Extensive experiments across multiple simulation environments validate the effectiveness of the proposed approach.", "questions": "1. Although the proposed world-model post-training strategy appears effective, it requires 50K training steps on 32 A100 GPUs over 4–5 days. Could the authors conduct additional ablation studies to analyze how this stage affects downstream performance—for instance, by reducing the size of the post-training dataset and evaluating the resulting impact?\n\n2. In Appendix D, the authors state: “This design enables policy learning for embodied control and spatial reasoning through language output.” Could the authors further clarify how UniVLA performs spatial reasoning tasks without being explicitly trained on such datasets? Is this ability inherited from the source EMU3 model after two-stage training, or were spatial reasoning datasets incorporated during post-training?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper presents a unified VLA framework that discretizes all modalities into tokens, resulting in a shared representation across visual, textual, and action modalities.\n2. Comprehensive experiments are conducted in both simulated and real-world scenarios, validating the effectiveness of the proposed framework and analyzing the transferability of different visual post-training strategies.\n3. The model is further evaluated beyond robotic manipulation, including autonomous driving tasks, demonstrating the generalization ability of the proposed approach.", "weaknesses": "1. Inference speed – The model is built upon Emu3 with 8.5B parameters and employs next-token prediction for action generation. This results in slow inference and limits its ability to handle tasks requiring high responsiveness. The authors are encouraged to provide detailed inference speed comparisons with other VLAs such as OpenVLA, π₀, and UVA.\n2. While the paper presents a strong discrete-token–based autoregressive framework, it lacks systematic comparisons or discussions with continuous-action (e.g., π₀, DexVLA[1]) or hybrid VLA models (e.g., HybridVLA[2]). Continuous or hybrid approaches often achieve smoother control and better precision, and an in-depth analysis of these trade-offs would strengthen the paper.\n3. The proposed UniVLA predicts the next chunk of actions conditioned on interleaved observation and state history, while most baseline VLAs rely only on current observations. This may create an unfair comparison. The authors should include stronger baselines that also utilize history information (e.g., Long-VLA[3]) to ensure fair comparison.\n\n\n[1]. DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control\n[2]. HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model\n[3]. Long-VLA: Unleashing Long-Horizon Capability of Vision Language Action Model for Robot Manipulation", "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": "MWVBo46mAX", "reviewer": "Reviewer_nxe1", "summary": "In this paper, the authors aim to demonstrate that existing VLA methods with language-centric paradigm have difficulty in learning of cross-modal represemtations, temporal and causal dependencies across the perception-action loop. The authors introduce UniVLA, which jointly models vision, language, and action through autoregressive sequence learning. The effectiveness of the method is validated through experiments in both simulation and real-world settings.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "1. This paper proposes a novel paradigm-UniVLA-that models all three modalities as discrete tokens within a shared autoregressive sequence. This represents a significant conceptual advance beyond traditional late-fusion or modality-specific architectures, enabling deeper cross-modal interaction and joint representation learning.\n\n2. The proposed world-model post-training via video-based supervision substantially enhances temporal reasoning and data efficiency, demonstrating a clear methodological contribution.\n\n3. The model achieves state-of-the-art performance across multiple simulation benchmarks and demonstrates strong generalization to long-horizon tasks.", "weaknesses": "1. Although the simulation results are impressive, the study lacks a comparison against established baseline models on real-world tasks. Robustness under noisy sensory inputs remain insufficiently validated. \n\n2. Why does employing action prediction within this framework during post-training adversely affect model performance?\n\n3. The 8.5B-parameter model exhibits significant latency in real-world deployment (evidenced by robotic arm stuttering in the video), critically impairing its capacity for real-time temporal information processing. Can this framework be successfully applied to other VLM with fewer parameters?", "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": "HdDjbpv0op", "reviewer": "Reviewer_Wn1i", "summary": "This paper introduces a novel architecture, UniVLA, which encodes three modalities—image, text, and action—into discrete tokens. This enables joint modeling in an autoregressive manner, achieving better cross-modal alignment. Based on this multi-modal discrete token autoregressive paradigm, UniVLA can unify the world model training and visuomotor policy within a single model. It also demonstrates that world model training improves policy learning, especially in long-horizon tasks and OOD scenarios. The authors achieved promising results on classic VLA benchmarks, including CALVIN, LIBERO, and Simpler-Env, demonstrating the effectiveness of their method.", "questions": "- Have the authors analyzed and compared different action encoding strategies? Within the autoregressive action-prediction paradigm, there are many ways to encode discrete tokens (e.g., FAST, VQ-VAE), and one could also adopt continuous latent encodings.\n- Did the authors incorporate multimodal understanding tasks within this multimodal discrete-token modeling paradigm? In the leftmost panel of Figure 1, we notice an illustration of a multimodal grounding task, yet Section 3.2 does not describe the corresponding training procedure.\n- Further discussion on architecture. Modeling discrete tokens across the three modalities is meaningful for aligning them, but it may be insufficient for VLA. For example, in PI-0.5, the first training stage jointly models vision, language, and action (with images not discretized and actions discretized via FAST), yet the second stage still introduces an action expert to produce actions, ensuring precise action modeling. Have the authors examined the difference between predicting actions solely with a VLM and adopting a dual-system–style architecture?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- The UniVLA architecture is meaningful: it discretizes images, text, and actions into tokens and trains them under a unified autoregressive paradigm. This enables stronger cross-modal alignment via diverse proxy tasks, such as world-model training, policy learning, and multimodal understanding.\n- The authors demonstrate that world-model training is an effective auxiliary task that benefits downstream policy learning.\n- UniVLA achieves competitive results on several standard benchmarks, showing improvements over some classic VLA models.", "weaknesses": "- Concern about inference speed. The authors use Emu3-8.5B as the autoregressive base, which is non-trivial in size for a VLA backbone. Moreover, action prediction requires generating a sequence of tokens, which inevitably slows inference. This is especially problematic in real-robot experiments, where limited on-device compute leads to a low action-prediction rate. The authors should further discuss UniVLA's deployment in simulation and on real hardware, its baseline compute requirements, and the concrete inference frequency.\n- Limitations of real-world experiments. As a VLA model, its performance in the real world is a crucial metric of its quality. However, the experimental section only outlines the basic setup of the real-robot evaluation in L454–457, without reporting UniVLA’s success rate on real tasks or comparing its real-world performance against other competitive VLA models. This omission hinders a fuller assessment of UniVLA’s contributions.\n- The authors should discuss how much of the current performance gain stems from Emu3's inherent multimodal discrete-token modeling. While we acknowledge that modeling vision, language, and action uniformly as discrete tokens is a reasonable approach, would the performance remain comparable if this paradigm were transplanted to other backbones? Notably, OpenVLA and Pi-FAST were not pretrained with such multimodal discrete-tokenization prediction.", "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": "PGESkdNUWO", "reviewer": "Reviewer_6H2r", "summary": "The paper “UniVLA: Unified Vision-Language-Action Model” introduces a fully tokenized, autoregressive architecture that jointly models vision, language, and action within a single transformer-based sequence framework. Unlike conventional VLA systems that fuse pretrained visual encoders with language-conditioned action heads, UniVLA treats all three modalities as discrete tokens within a shared vocabulary, enabling unified cross-modal reasoning and flexible task conditioning.\nThe framework operates in two stages: (1) Post-training (world modeling): learning causal video dynamics and temporal prediction from large-scale robot-centric videos without action supervision. (2) Fine-tuning (policy learning): conditioning on interleaved visual and textual tokens to predict action tokens.\n\nThe approach achieves state-of-the-art results across CALVIN (4.63 average length vs. 4.49 for RoboVLMs), LIBERO (95.5% vs. 85.5% for π₀-FAST), and SimplerEnv (69.8% vs. 42.7%). It also transfers to real-world ALOHA robotic tasks and autonomous driving (NAVSIM), showing versatility of the unified token-based modeling.", "questions": "- How sensitive is UniVLA to the specific choice of discretization (VQ encoder, DCT-based FAST)? Could learned continuous latent codes outperform these fixed tokenizers?\n\n- During post-training, how much of the improvement stems from video scale vs. the world model objective itself?\n\n- Can UniVLA generalize to reinforcement learning settings where rewards guide action prediction rather than imitation?\n\n- How does token-level autoregression scale with longer action horizons (e.g., >1,000 tokens per rollout)?\n\n- Is there any observed degradation when transferring from simulated to real-world data, especially under domain shifts in lighting and textures?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- UniVLA’s key novelty lies in representing vision, language, and action as discrete tokens under a shared autoregressive framework. This eliminates separate encoders and heads, resulting in a conceptually elegant, flexible design. The framework’s success on both robotics and driving underscores its generality.\n\n- The paper demonstrates that pretraining on large-scale video prediction (without action labels) significantly enhances downstream policy efficiency and long-horizon reasoning. Table 4’s analysis is particularly convincing, showing +45–70% relative improvement over action-only post-training.\n\n- UniVLA consistently outperforms prior VLA baselines (π₀, FAST, OpenVLA, GR-1, RoboVLMs) across three simulation suites and two real-world domains. The inclusion of both robotic manipulation and driving tasks makes the evaluation unusually broad for a single model.\n\n- The paper systematically analyzes data efficiency (Table 5a), training efficiency (Table 5b), world model variants, and historical context. The insights—for instance, diminishing returns beyond 2-step history—are practical for future VLA design.\n\n- Demonstrating performance gains on the physical ALOHA robot (pouring, folding) and quantifying inference latency (≈3s per 20-step chunk) is commendable. Few VLA papers provide such detailed real-world evaluation.", "weaknesses": "- The model borrows heavily from existing architectures (Emu3 for transformer backbone, FAST for action tokenization, VQ-VAE for image tokens) and primarily unifies them under one autoregressive umbrella. While impactful, the approach feels more like a pragmatic consolidation than a paradigm shift.\n\n- The paper includes numerous benchmarks, ablations, and cross-domain studies—sometimes at the expense of clarity. A concise ablation visualization or conceptual diagram summarizing findings could make the narrative more digestible.\n\n- The authors claim that token unification enables stronger “causal reasoning,” but this is not analytically substantiated. There’s little formal justification for why autoregressive modeling over discrete tokens inherently captures causal structure better than multimodal fusion.\n\n- The method’s scalability depends on massive video post-training (622k videos, 32×A100 GPUs for 5 days). The claim of “data efficiency” holds mainly at fine-tuning, not at pretraining. Real-world feasibility for smaller labs remains questionable.\n\n- Although the paper reports strong quantitative results, it does not deeply analyze potential loss of control granularity introduced by discrete action tokenization, especially for fine-grained manipulation tasks (e.g., folding clothes).", "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)