X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model
Authors: Jinliang Zheng
Jianxiong Li
Zhihao Wang
Dongxiu Liu
Xirui Kang
Yuchun Feng
Yinan Zheng
Jiayin Zou
Yilun Chen
Jia Zeng
Tai Wang
Ya-Qin Zhang
Jingjing Liu
Xianyuan Zhan
Venue: ICLR 2026 Poster (2026)
Keywords: robotics; vision language action model; prompt learning; heterogeneous pretrainining
Primary area: applications to robotics, autonomy, planning

Abstract: Successful generalist Vision-Language-Action (VLA) models that rely on effective training across diverse robotic platforms with large-scale, cross-embodiment, heterogeneous datasets. To facilitate and leverage the heterogeneity in rich, diverse robotic data sources, we propose a novel Soft Prompt approach with minimally added parameters, by infusing prompt learning concepts into cross-embodiment robot learning and introducing separate sets of learnable embeddings for each distinct data source. These embeddings serve as embodiment-specific prompts, which in unity empower VLA models with effective exploitation of varying cross-embodiment features. Our new X-VLA, a neat flow-matching-based VLA architecture, relies exclusively on soft-prompted standard Transformer encoders with an enhanced encoding pipeline, enjoying both scalability and simplicity. Evaluated across 6 simulation environments as well as 3 real-world robotics platforms, our 0.9B instantiation-X-VLA-0.9B simultaneously achieves state-of-the-art performance over a sweep of benchmark suites, demonstrating superior results on a wide axes of capabilities, from flexible dexterity to quick adaptation across embodiments, environments, and tasks.

Published as a conference paper at ICLR 2026
X-VLA: SOFT-PROMPTED TRANSFORMER AS SCAL- ABLE CROSS-EMBODIMENT VISION-LANGUAGE- ACTION MODEL
Jinliang Zheng Jianxiong Li Zhihao Wang*.;, Dongxiu Liu', Xirui Kang', Yuchun Feng', Yinan Zheng', Jiayin Zou', Yilun Chen², Jía Zeng', Ya-Qin Zhang', Jiangmiao Pang", Jingjing Liu', Tai Wang Xianyuan Zhan
Tsinghua University
Shanghai AI Laboratory
3 Peking University
* Equal Contribution 'Corresponding Author
wangtai 6p jlab. org. & zhanx
Project website: httgg./mmn.aaa...aaaaaaaa.....m www.:aaannnnannsssnnnnaeee edd.
dream. github. io/-VVLL..
ABSTRACT
Successful generalist Vision-Language-Aen actinn (VLA) models rely on effective training across diverse robotic platforms with large-scale, cross-embodiment, het- erogeneous datasets. To facilitate and leverage the heterogeneity rich, diverse robotic data sources, propose novel Soft Prompt approach with minimally added parameters, by infusing prompt learning concepts into cross- cross-embodiment robot learning and introducing separate sets of learnable embeddings for each distinct data source. These embeddings serve as embodiment-speee which in unity empower VLA models with effective exploitation of varying cross- embodiment features. Our new X-VLA, neat www-matching-base VLA archi- tecture, relies exclusively on soft-prompted standard Transformer encoders with enhanced encoding pipeline, enjoying both scalability and simplicity. Evalu- ated across 6 simulation environments as well as real-world robotics platforms, 0.9B instantiation-X VVLA-00.9 simultaneously achieves state-of-the-as per- formance over a sweep of benchmark suites, demonstrating superior results on wide range of capabilities, from flexible dexterity quick adaptation across embodiments, environments, and tasks.
prompts,
X-VLA simple scalable crose-embodiment foundation model
(Scetal)
Simpler
[Object
Libero
3 Bimplor (Piycta
model architecture
87.8
Soft Prempt
"fold dash
Propriol tholay action
Libero 87 (Long)
Bimpler Flectal-MMM
Agibor-3
Soft Prompt (SP)
Agilex33
Protrained VLM
Vision Encoder
(ABC-O)
7ea
AMVIIM
Franka-1 view
Soft-prompted Self-atiention Transformer Block
Rozonnn2
VLABunch
Leamable Tokons
Max of 23 seiting models
X-LA-0 98 (Curs)
One Model Beat Them
Scalable Cross- embodiment Protraining
Strong Adoplation Capabillty
Precktton Ererr
Stable training
Acaot new robots
dynamics,
LoRA
Model StreDDate
only 1% Panemeters (9M)
Bource/Date Size Enrrr
Prediction
Training Steps
VIAABeedd Robotwin2
Enhanced Pretraiing
Scalable Training
Crose-rabct Adapt
Crass-task / env Acaot
Dewterous Task
Profix Turting Enhancement
Figure I: X-VLA employs distinctive learnable embeddings, referred to as soft prompt, to effectively address the heterogeneity present in coos--ebbodiment datasets. Combined with stacked self-attention transformer blocks and enhanced encoding pipeline for multimodal inputs, this design offers scalable framework for integrat- ing diverse pretraining datasets and adapting to domain speciffc applications. Evaluated across simulation benchmark including five manipulation benchmarks and one autonomous driving benchmark, as well as 3 real- world robots, X-VLA achieves SOTA performance over most benchmark suites and real-world robotic tasks.

---

Published as a conference paper at ICLR 2026
1 INTRODUCTION
It has long been a central ambition in the robotics community (Brohan et al., 2023; 2022) to build autonomous agents that are capable of: (1) flexibly following arbitrary human instructions (Wang et al, 2025b), and (2) dexterously operating across diverse environments as well as disparate embod- iments (Black et al., 2025a). In light of recent success of Large Language Models (LLMs) (Achiam et al., 2023; Bai et al., 2023a; Touvron et al., 2023) and Vision-Language Models (VLMs) (Li et al., 2024a; Bai et al., 2023b), one promising direction is to extend these advanced architectures to roboties through the incorporation of precise action modalities, thereby giving rise to Vision- Language-Action (VLA) models (Kim et al.). The inherent expectation is that such large VLA models can marry out-of-the-box generalization with robust manipulation capabilities, from sim- ple pick-and-place operations to complex dexterous tasks (Black et al., 2025b; Team et al., 2025; NVIDIA et al., 2025).
The success of VLA models, particularly their ability to rapidly adapt to out-of-distribution (OOD) domains, hinges on pretraining with large and diverse robotics datasets that encompass a broad spectrum of robotic system architectures and a wide range of task scenarios (O'Neill et al., 2024; Lin et al., 2025). A key challenge here that VLA models face substantial heterogeneity from hardware configurations to data collection strategies (Wang et al., 2024b). Such heterogeneity manifests not only in embodiment-speen tions such camera settings, visual domains, and task distributions (Doshi et al., 2024b; Shi al., 2025b; Zhen et al., 2024). These various dimensions of diversity induce severe distributional shifts as well as significant semantic misalignments across embodiments, confusing the model and ulti- mately leading to unsatisfactory pretraining and adaptation performance (Zheng et al., 2025; Liu et al., 2025b). Existing VLA methods primarily assign distinct action decoder heads to accommo- date embodimeen--ppecffic action spaces as their main focus (Black et al., 2025a; NVIDIA et al., 2025), with other critical sources of heterogeneity ineluctably overlooked. Reconciliation among these disparate configurations, however, is crucial for proprioceptive-awai tilling shared knowledge from heterogeneous, mixed-domain datasets, which persistently remains an unsolved problem due to: (1) the inconsistency across hardware platforms, (2) the absence of stan- dardized data collection protocols, and (3) the inherent domain shifts that arise across embodiment and environment barriers (O' Neill et al., 2024).
action spaces (Liu et al., 2025b), but also in setup varia-
reasoning and for dis-
We demonstrate that these obstacles can be effectively overcome with minimal human effort in- volved, by allowing VLA models to learn domain-specific hardware configurations through simple Soft Prompi mechanism (Lester et al., 2021). Inspired by insights from meta-learning and multi- task learning, recast diverse hardware configurations and data types in robotics domain into the mold of task-specific features, which can then be effectively captured through prompt-learning tech- niques (Wang et al., 2023; Liu et al., 2023c; Khattak et al., 2023; Liu et al., 2023b). Specifically, to model the varying dimensions of heterogeneity as aforementioned, we assign a set of learnable embeddings to each data source Soft Prompts. These embeddings provide heterogeneity- guidance for structuring the VLA representation space from early stages of feature fusion, which endows the VLA model with an enhanced capacity exploit and consolidate cross-embodiment variations, improving generalization across different hardware and task configurations.
Formally, we introduce X- VLA, generalist www--matthinn--baa based VLA framework built upon Soft- prompted Transformer, designed to operate seamlessly across heterogeneous platforms. Through Soft Prompts, X-VLA can be guided by explicitlyleeanee individual hardware setups to accommo- date various structures of system and data. With versatile architecture well-equipped for simulta- neously encoding multi-view images, language prompts and proprioceptive features, X-VLA allows scalable VLA training, by simply stacking standard Transformer encoders (Vaswani et al., 2017) for multimodal feature fusion and precise action generation.
Extensive experiments demonstrate that Soft Prompts outperform other state-of-the-art (SOTA) methods in handling various heterogeneity dimensions. The X-VLA architecture exhibits a stable learning process and superior asymptotic performance, offering favorable scaling capabilities to- wards larger model size and mixed-robot datasets.
We implement X- X-VLA-0.9B, 0.9B instantiation of X- X-VLA, trained with carefully designed data processing and learning recipe. The overall training pipeline consists of two phases: Phase I: Pretraining. We pretrain x- X-V---0 curated heterogeneous data mixture comprising 290K
2

---

Published as a conference paper at ICLR 2026
episodes (Khazatsky et al., 2024; Wu et al., 2024; Bu et al., 2025), spanning seven platforms across five types of robotic arms, ranging from single-arm to bi-manual setups. By leveraging soft prompts to absorb -mbodiment-ppeccfiie variations, the model learns embodiment-agnootii generalist pol- icy. Phase II: Domain adaptation. X-VIA--0.8 is adapted into a deployable policy for a target domain. A new set of soft prompts is introduced and optimized to encode the hardware configu- ration of the novel domain, while the pretrained backbone remains frozen. With these prompts in place, the policy then effectively specialized the new embodiment through finetuning.
We conduct extensive experiments to evaluate the adaptation capabilities of the proposed X-VLA- 0.9B across diverse embodiments, environments, and tasks. Remarkably, X- X-VLA-0.9B achieves new state-of-the-ee results on six simulation benchmarks, including five robotics benchmarks and one autonomous driving benchmark, as well as on three real-world robot platforms. Furthermore, with only 1 1,200 demonstrations, the model excels at dexterous cloth-folding manipulation in the real world, achieving an average throughput of folding cloth in under two minutes paapuutes-oompaaablaaaaaa to closed-source models with substantially more parameters trained on larger datasets. In addition, we demonstrate that Phase adaptation can efficiently realized through parameter-efficient fine- tuning (Hu et al., 2022) at minimal training cost. Specifically, with the aid of previously learned prompts, X- X-VLA-0.9B achieves a 93% success rate on LIBERO and 54% on Simpler-WidowX WidowX by tuning only 1% of the model parameters (9M). These results are comparable no (Black et al., 2025a), despite requiring 300x fewer parameters (9M vs. 3B).
2 PRELIMINARY
VLA models. VLA models class of models that unify multi-modal understanding and action generation for robotic control (Black et al., 2025a; NVIDIA et al., 2025). Typically, VLA models are initialized from VLMs pretrained on large-scale image-text corpora, and then finetuned robotics
dataset containing expert trajectories: D { Tj {(o,, ) where On denotes multi- modal observation at step (e.g., visual input, language instruction, proprioceptive states), and an is its corresponding expert action. The training objective is typically framed as behavior cloning, where the policy (on) parameterized by 0 is optimized to predict the demonstrated action chunk An = [an, an+1, .... an+r] where T denotes the chunk size, by minimizing a suitable supervised loss ((.) as: (0) = A,o [<( An)].
Flow-matching policy. Instead of directly predicting the expert action chunk A from observation o, flow-matching policies commonly learns a velocity field (Lipman et al., 2023) that transports a noise sample to the target action chunk. For instance, one can generate an action A by starting from an Gaussian noise A ~ (O, I) and iteratively refining through a velocity field (o o, parameterized by neural network using ODE solvers such -uler-Maruyama method: A'+Ar A' + og A", ,,, i). Here, / E [0, 1] is a continuous time variable. To train the velocity field, we the OT (optimal transport) path (Lipman et al., 2024; 2023), which aligns the velocity with the linear interpolated path between noise and expert data:
CDM (0) EE (o,A)DD [Ilo(AA',,)) (A (A- ]]
where A' (1 t) + tA, 24 is uniform distribution. By minimizing CFM the policy learns to progressively transport random noise toward expert chunks conditioned on observations.
Heterogeneity in cross-embodiment training. Training mixed data recipes composed of H H
heterogeneous datasets, DH = =(D,) is essential for developing generalist VLA models (Doshi al., 2024a; O'Neill al., 2024). Such training enables the aggregation of knowledge across diverse robots, facilitating fast cross-embodiment transfer and out-of-the-box deployment on unseen platforms. Each dataset, D, is collected under a specific hardware configuration, h2 H. where H. represents the space of possible hardware setups, such as arm kinetics, control interfaces, camera setups, and deployment scenarios. These introduce significant heterogeneity, not only in low-level action signals and distributions, but also in high-level visual understanding, which can result in poor pretraining and adaptation if not effectively addressed (Wang et al., 2024b; Zheng et al., 2025).
3

---

Published as a conference paper at ICLR 2026
Domain-spectic Action Projection
HPT-style Projection
(c) Language Prompt
Soft Prompt (Curs)
Muti modal Tokens
Robet Descritton
"Th roporis
Soft Prompts
Muttimodal Tokens
Muts-modal Tokans
Multi-oddal Tokens
Queried by
by ID
Dataset ID
VLA Madel
VLA Model
Model
Mocial
Actin
Action
Action
Queried Datazet
Quoried
Queried Dataset
Queried Dataset ID
Existing Solution
Potential Solutions
Our Solution
Figure Comparison among four methods handling heterogeneity in cross-embodiment training.
Validation Error w.r.t Training Steps
0.072
7%
Embodimen
Source
Selup
0%0%%
8%%)
30HZ AGIDOT-Beta
0.088
6.7%
Franvaj1 5..155... 5.8%)
15hz
Droid
LetrWris
Predition Eree
0.064
200K High- Qual-Qually Episodes
Frankait 5.8%)
18
Droid
RightWWiss
0.080
Franket8 7%)
30Hz
RoboMind
Top
Dual-Frnika(0.80)
30K0
RoboMind
FromtWrist
0.066
107Tuge
Lar Langueee hrogg
UR-5(8.7%)
J0hiz
RoboMind
Top
0.052
120
150
180
200
Agiisgs3 7%6
2012
RoboMind
FrontWiist
Triining Stapa (x1000)
Figure 3: The recipe for mixed data used in pretraining experiments.
Figure 4: Training curves for various methods of handling heterogeneity.
3 HETEROGENEOUS SOFT PROMPT LEARNING
To address heterogeneity, we conduct a comprehensive empirical study to explore potential design choices, as shown Fig. 2. We follow Reuss et al. (2025); NVIDIA et al. (2025) to establish a standard dual-system architecture as our starting point, which leverages VLMs for multimodal per- ception and a DiT-style decoder for action generation. mixture from recent high-quality sources, including AGIBOT-beta (Bu et al., 2025), RoboMind (Wu et al., 2024), and Droid (Khazatsky et al., 2024). This dataset spans seven hardware setups across five robots, ranging from single-arm to bi-manual setups, providing sufficient scale and diversity necessary for generalist policy training. We evaluate all methods using a fully aligned training recipe ensure fair comparison. See Appendix for more training details.
Fig. 3, we construct a heterogeneous data
(a) Domaii-specific action projection. This strategy addresses heterogeneity by assigning sep- arate projection heads the model output map action tokens into embodiment-specife action spaces. While this approach is widely used in prior embodied foundation models (Black et al., 2025a; NVIDIA et al., 2025; Team al., 2025; Zheng et al., 2025; Liu al., 2025b), its effect is limited to the final action generation stage. Consequently, it fails to encourage embodiment- aware reasoning earlier in the pipeline and overlooks other critical sources of heterogeneity, such as variations in different camera setups and task distributions. To circumvent these limitations, we identify three representative strategies that improve pretraining stability on heterogeneous datasets, as summarized in Fig. 2. We analyze these strategies in the following discussion, with additional experimental attempts reported Appendix E.
(b) HPT-style projection. Inspired by Wang et al. (2024b), this approach aims to mitigate domain discrepancies in observation inputs and promote generalist reasoning by mapping observations from distinct domains into shared representation space. Specifically, domain-specific projection layers are also applied on top of multi-mon modal inputs to align them before being fed into the backbone.
(c) Language prompts. Another strategy leverages the language reasoning capabilities of pretrained VLMS. In this case, natural language descriptions of hardware configurations h; are provided as additional inputs, enabling the model to attend embodimenn-sppecffe variations through textual descriptions explicitly. The language template used are summarized in Tab. 12.
(d) Soft prompts. Finally, we investigate a soft-prompt method that follows the meta-learning and multi-rask learning philosophy (Finn et al., 2017; Liu et al., 2023c) by introducing domain-specific learnable parameters pH { absorb heterogeneity across data sources. p; is expected
to encode the underlying hardware configuration: pi @(h:). where + H IR denotes
4

---

Published as a conference paper at ICLR 2026
latent mapping from hardware configurations the prompt space. Notably, o is not predefined as in language prompts (c) but is randomly initialized and then implicitly optimized through end-to- end training. These soft prompts injected into the model at the early stage of action generation, automatically guiding the backbone toward embodiment-awware learning.
While (b) and (c) conceptually appealing, they suffer from notable limitations. HPT--style pro- jection introduces different projection layers in the middle of the observation processing, which frequently alter feature distributions and are prone to corrupting pretrained VLM representations. often resulting in unstable training dynamics. Language prompts, on the other hand, rely on care- fully scripted textual descriptions of hardware configurations, which greatly hinder adaptability and scalability in practice. In contrast, soft prompts offer flexible and scalable solution for encoding domain-specific hardware configurations. They marry the advantages of both (b) and (c), integrating smoothly with the backbone while preserving pretrained representations and eliminating the need for handcrafted annotations. The empirical results in Fig. 4 confirm that Soft Prompts consistently achieve much more robust and stable training across heterogeneous datasets. We further discuss additional unsuccessful attempts to address heterogeneity in Appendix E; while these approaches did not succeed, we report them inform and inspire future research.
4 X-VLA: SOFT-PROMPTED TRANSFORMER ENHANCED VLA MODEL
Building on Soft Prompts, we introduce X-VLA, a neat VLA architecture designed for stable pre- training on heterogeneous datasets and efficient adaptation on new domains. In this section, first present the overall architectural design, followed by several key techniques for large-scale pretrain- ing. The complete ablation path is provided in Tab. 1, which highlights the contributions of the components introduced in this section.
4.1 ARCHITECTURE
We aim to build streamlined encod- ing pipeline for complex multimodal in- puts. Beyond Soft Prompts, X-VLA han- dles (/) high-dimensional inputs (multi- view visuals and languages), and (2) low- dimensional states (proprioception and ac- tion tokens). Due to substantial discrepan- cies in both semantics and dimensionality across these modalities, we employ dedi- cated encoding strategies to align them ef- fectively, after which vanilla transformer stacks suffice scalable policy learning. Below, we detail the encoding pipeline with the complete architecture and addi- tional design explorations are provided in Fig. 5 and D.
Domain spariiic Parameters 04% Parametar)
Soft Promot Library
Inpui Linear Projection Library
Output Linear Projection Library
Query
Soft Prompt
Imput Projection
Output Projection
Model Forward
Lang
Path
(Option)
3 VIT
Pretrained VLM
Repaat Coneat
Encoder
Shared Linear Projection
Input Projection
Soht Prompt
Mi Milli-moda Tokens
Control Tokent
xN
Standard Sell-attention Transtomer Block
(1) digeesiimeii observation stream. High-dimensional inputs in- clude multi-view images Img = (img.). ). together with languages L specifying task objectives. Unlike most prior ap- proaches (Black et al., 2025a) that directly feed all views and instructions into VLMMs, we disentangle the streams by assigning distinct encoders. A pretrained VLM en- coder (Florence-Large (Xiao et al., 2024)
Output Projection
Action Chunk
Figure 5: Illustration of our model architecture. Most pa- rameters are shared across embodiments, with only the soft prompt and the action-related input/output projections re- maining domain-specific (0.04% of total).
in X- VLA) used for the main vision- Laiiual stream (fixed-view and instruction), while auxiliary views such as wrist-views are processed with shared vision backbone. This design alleviates the semantic gap between generic languae reasoning and embodied reasoning: fixed-camera views provide stable, informative context for high-level task reasoning: whereas crist-camera
5

---

Published as a conference paper at ICLR 2026
imputs, though noisy and fast-changing, offer critical cues for fine the-grained manipulation and thus encoded separately from the language stream.
(2) Low-dimensional proprioceptive-action stream. The proprioceptive states Rt, such as joint positions and end-effector poses, provide uubodiment-speciife grounding for reasoning and control. The action-related tokens A' consist of noisy action samples used for flow-matching generation. Since both R' and A' are compact vectors with closely related physical semantics, we concatenate them along with their corresponding time embeddings & within the flow-macching pipeline. The fused representation is then projected into a high-dimensionn feature space through a lightweight linear layer, enabling early fusion with other modalities and ensuring robust proprioceptive-tenaeee grounding during both training and inference.
(3) Soft prompts. The well-encoded multimodal inputs are concatenated with soft prompts, which are typically sequence of N learnable tokens. Each domain is associated with its own set of soft prompts and selected according the dataset id. For instance, our pretraining setup as shown in Fig. 3, we maintain 7 distinct sets of learnable tokens corresponding to the 7 datasets. The concatenated token sequence then passed into a standard Transformer encoder, which performs cross-modal reasoning and generates the corresponding action outputs.
4.2 CUSTOMIZED TRAINING RECIPE
To fully incentivize the potential of X- X-V,, we introduce carefully designed learning engineering to enhance both stability and effectiveness for X- V-V training. We provide an overview of our training recipe and outline several key techniques that are crucial for the stable and efficient training.
4.2.1 PRETRAINING AND FINETUNING PIPELINE
For pretraining, the backbone and the soft prompts pH are jointly optimized under the flow- matching objective LFM Please refer to Appendix G for detailed pretraining hyperparameters. After pretraining, the backbone becomes embodiment--gnostio foundation capable of rapid adaptation across heterogeneous robots. To deploy this model on novel domains with new hardware configura- tions fnew, we propose a lightweight two-step adaptation procedure:
(1) Prompt warm-up. We introduce new sets of learnable prompt Pnew E R for hnew The prompt is first warmed up while keeping the pretrained weights frozen. By doing so, prompts projected to exploit pretrained embodiment-agnostic features, offering good starts for next-round joint training.
(2) Joint policy adaptation. Then, we jointly optimize both the backbone and the warmed-up prompt, jointly adapt to new domains. This two-stage process first lets Pnew encode the hardware- specific setups of knww. and then finetunes the full policy for effective specialization, sharing the same philosophy used to adapt LLMs to VLMs (Liu et al., 2023a; Li et al.).
Custom learning rate (LR). A key stabilization technique in both pretraining and adaptation is the use of a reduced learning rate for the soft prompts as well as for the vasion-la modules that respond for encoding visual and linguistic inputs. This adjustment reduces the risk of catastrophic drift from pretrained representations, an issue also noted by (Reuss et al., 2025; Driess et al., 2025), leading to smoother optimization during pretraining and more reliable specialization when adapting to novel embodiments. It effectively bridges the general knowledge encoded in vision lsiion-language models with the one-grained spatial localization and action grounding required by VLA models.
4.2.2 ENHANCED DATA PROCESSING
Aligned action representation. Actions are the core supervision signals for VLA models, with their quality directly shaping training outcomes. Therefore, we standardize the action space into end-effector (EEF) pose representation comprising: (I) the Cartesian EEF xyz position, (2) the absolute EEF rotation encoded using the Rotate6D representation (Zhou et al., 2019) to avoid the discontinuities inherent in Euler angles and quaternion representations, and (3) the discretized binary state of the gripper. The position and rotation optimized using sawuun--uureeeeeeee while the gripper state is optimized with binary-cross-entrre (BCE) loss. This ensures consistency across embodiments, providing robust supervision for generalizable policy learning.
(MSE) loss,

---

Published as a conference paper at ICLR 2026
Table 1: The ablation path for each components in Section 4. We evaluate the pretraining (PT) validation adaptation (AD) success rates on Simpler- WidowX benchmark (Li et al., 2025b). Green, Red Gray denote positive, negative, moderate effects, respectively, Bold scores are SOTA results. We can see that naively training on heterogencous data Icads to degradation. Also, as validation error decreases during pretraining, the adaptation success rate increases progressively, demonstrating strong correlation between the two. Therefore, we the validation error as a proxy for pretraining performance throughout this paper. is evident that cach components Section contributes positive improvements for pretraining.
Type
Improvements
Val Error (PT)
Acc (AD)
Baseline Model (wo PT)
Florence-base + Standard DiT-buse
4.1
+Custom LR (w/o PT)
39.6 (+35.5)
Pretraining Technique (Section 4.2.1)
*ecterogencous PT
0.11
25.0 (-14.6)
+ Action alignment
Data Processing (Section 4.2.2)
Intension abstraction
0.077 (00033)
50.0 (425.0)
Balanced data sampling
+Replace DiT with Transformer encoder
0,071 (-0.066)
47.9 (-2.1)
Architecture Design (Section 4.1)
+Encoding pipeline
0.053 (-0.11)
64.6 (+16.7)
+Soft-prompt
0,041 (-0012)
73.8 (49.2)
+Scaling up
0.032 (.0.099)
89.6 (+15.8)
Finctuming Technique (Section 4.2.1)
Two-step adaptation
0.032
95.8 (16.2)
Intention abstraction through temporal downsampling. While low-level action trajectories pro- vide the precise manipulation signals required for deployment, they are often too fine-grained and may contain lots of noisy movements due to human randomness, thus are not suitable for achieving high-level grounding and intention modeling for pretraining. To mitigate this issue, we temporally downsample demonstrations to construct abstract representations rather than predicting the full end-effector pose at every time step, the pipeline is designed to gener- ate a sequence 30 anchor points that summarize the intended trajectory over the next 4 seconds. The choice of the 4-second prediction window is based extensive preliminary experiments, with detailed results provided in the Appendix D.4.
action intentions. Concretely,
Balanced data sampling strategy. In contrast the common round-robin data sampling strat- egy (Wang et al., 2024b), we observe that stable training requires a carefully designed data shuffling pipeline. We shuffle samples not only across different domains but also across trajectories within each domain, ensuring exposure to a diverse and balanced data mixture at every iteration. This effectively mitigates distributional bias and reduces overfitting dominant domains, facilitating smoother convergence during large-scale pretraining.
4.3 IMPLEMENTATION DETAILS OF X- X-VL9-0.99
In this paper, we build 0.9B parameter instantiation of X-VLA using heterogeneous embodied data sources, as illustrated in Fig. 3. Specifically, X-VLA adopts Florence-2-Large (Xiao et al., 2024) its VLM encoder, given its strong performance in visual grounding and general vision language reasoning capabilities that are essential for embodied tasks (Reuss et al., 2025). X-VLA- 0.9B employs standard Transformer backbone with 24 layers and hidden size of 1024 for action generation. A key component of the framework is the soft prompt, whose length is set to 32, which determined through systematic scaling experiments across model sizes (Fig. 6). Additional pre- training and post-training details are provided in Appendix G.
EXPERIMENTS
In this section, we conduct extensive experiments to investigate 1. Does X-VLA exhibit scaling properties along model size, data diversity, and data scale? 2. Can X-VLA specialize to novel domains with varied characteristics? 3. Do the soft prompts capture meaningful representations that reflect the heterogeneity of mixed data sources?

---

Published as a conference paper at ICLR 2026
Scallin
Stze
-1.30
0.88
Sealing Behavior Across Data Sources Scaling Benavior Across Data Size
Log Predtcton Eitor
-1.35
2
122hrdeph12
700-
768-dapp16
0.040
0.090
0.07
1024 12
E 3a.c0
Teeiation Dree
0,242 6.730
10844
-1.40
0.00
$ 0.00
0,000
-1.45
2:27
0.04
ugg
0g22
-1.50
0.00
2.5
2.8 Log GFLOPS
3.0
Length
Number Data Souroea
Siee Ex1ON)
Figure 6: With increased compute, data diversity, and data volume, X-VIA can output reduced validation prediction error which can lead to enhanced adaptation performance as discussed by Tab. 1.
Oross-embodiment Adaptation
Cross-anvironment and -task Adaptation Dexterous Task PEFT
Bridge
Simpler-Widoe WidowX Simpler Cemple-Frractal
Libero
Robo-win-220
Calvin
VLABench
Cloth-Folding
Figure 7: The evaluated setups in adaptation experiments, to evaluate different axes of adaptation capabilities.
5.1 SCALING EXPERIMENTS
First, we study the scaling behavior X-VLA along three axes: (1) model capacity, (2) data diversity, and (3) data volume. As shown in Tab. prediction errors observed during pretraining are strongly correlated with downstream adaptation performance. Therefore, we adopt the l error between pre- dicted actions (after fow-matching denoising) and ground-truth actions on held-out validation sets as our primary evaluation metric. The results are summarized in Fig. 6, with additional training de- tails presented in Appendix G. Notably, even the largest tested configuration, X-VLA-0. (hidden size 1024, 24 Transformer blocks), trained on 290K episodes from 7 data sources, the scaling trend shows no sign of saturation. This indicates that further increases along these three axes could yield additional performance gains. Due to resource constraints, we adopt the largest configuration, as the default model for subsequent experiments.
5.2 ADAPTATION EXPERIMENTS
We present one of the most comprehensive validation studies to date, evaluating X- VLA-0.9B across 6 simulation environments and 3 real-worke robotic platforms. See Appendix D for more results.
Simulation benchmarks. We evaluate on Libero (Liu et al., 2024a), Simpler (Li et al., 2025b), VLABench (Zhang et al., 2024a), RoboTwinn-2.0 (Chen al., 2025), Calvin (Mees al., 2022) and NAVSIM (Dauner et al., 2024). These benchmarks encompass hundreds of evaluation setups, spanning single-arm, bi-manual robotic systems, autonomous driving and assessing diverse axes of generalization, including cross-embodiment, cross-environment, and cross-task adaptation. Across FIVE benchmarks, we establish new SOTA, achieving substantial improvements over aggregated prior models. Remarkably, it attains over 90% success rates on several benchmarks, e.g., Simpler- WidowX (96%), Libero (98%). and Calvin- 1st stage. To the best of our knowledge, no prior model has reported such comprehensive evaluation paired with consistently significant gains, underscoring the superior performance of X-VLA-0.99 -0,9, which can become a strong baseline for future research to develop advanced models (please refer Appendix H for details).
Real-world experiments. We also evaluate X- VVLA-..99 on physical robotic platforms follow the BridgeData-v2 benchmark (Walke et al., 2023), the evaluation details can be found in Appendix and the results are reported in Fig. 8. Our X-VLA surpass other baselines across all five tasks, each for testing distinct axis of capability, demonstrating the superior adaptability of our X-VLA.
Dexterous cloth-folding task. We introduce challenging dexterous cloth-folding task that re- quires smoothing highly disordered cloth and folding neatly. To support this effort, we build a high-quality cloth-folding dataset on the bi-manual Agilex platform, termed Soft-Fold, which con- sists of 1,200 trajectories collected through a carefully designed pipeline. detailed description of both the task and the dataset is provided in Appendix F. Importantly, we will release the dataset to facilitate future research in dexterous manipulation. Leveraging this dataset for adaptation. our X- VLA -0. 9B model achieves throughput of nearly 100% success and 33 completed folds per
8

---

Published as a conference paper at ICLR 2026
Table 2: Comparison of specialize and generalize models on simulation benchmarks
Methoss
Size
VM
Simpler
VA
Widoxx
Spaiil
Obyect
LIBERO
Good
Calvin ARC
Fasy
RobeTwin-2.
Hand
VLABbach
Ang
NAYSIM
POMS
LMP
MoDE (Reess 2024)
0:43
$4.0
2.01
SuSTE (Black et al, 2024)
1B
76..
2.69
Glue (Bacc
SpuniaVLA (Qs 2025)
70.7
42.7
88.2
89.9
TrasrVL (7hing al, 2004b9
711
46.2
89.1
64.5
85.2
75.1
54.1
74.8
ThinkAd 2025)
65.1
43.8
85.3
91.4
FFC-VLA (Yan 2025)
658
64.6
86.2
87.0
MemoryVI.A (Stiie (Stiet sl, 2025s)
78
72.7
72.7
71.
98.4
98.4
96.4
91.4
96.7
Octo (feio Model Team stal. 20243
0:18
1688
1.10
354
78.9
85.7
$4.6
51.1
75.1
(Wo 2023)
Seer (Tias atal. 2025)
0.38
$7.7
4.25
UniAct (2heng al., 2025)
0.58
75.0
87.0
77.0
70.0
77.8
20056
PLOWER (Kano et 2025)
400
91.1
96.7
95.6
95.5
SmaTVLA (Shukor et 2025)
2B
930
94.0
91.0
77.0
85.8
GROOTNI (NVIDIA
2025)
48.4
94.4
(Black 2005g
56.8
178
95.8
98.8
95.8
46.4
16.4
TyrHAST (Pensch et 2025)
3B
61.9
00.5
79.5
95.4
96.8
19.6
141.2
$5.5
31.1
(Kin
76.5
QpeVLLA-OPF (Kim 2025)
31.3
97.6
98.4
94.5
DO-YLA (Linng et, 20125
7II
71.2
64.1
49.3
97.2
98.6
97.4
92.0
96.3
UniVI A (Wong 30158)
9n
69.3
95.4
98.8
95.6
94.0
98.4
4.41
Brisiigg
72.0
71.9
95.4
X-YLA (Ours)
0:98
30.4
75.7
95.8
95.2
98.6
97.8
97.6
98.1
4.43
70.0
39.0
$1.1
87.3
DUTS
opendia
octo
P12
ACT (Tran from Soraco))
pirnt
18
Simple Manipulation (WidowX)
Dex Manipulation (Agilex)
PEFT (AIRBOT)
Figure 8: Soal-World Evaluation Results. We evaluate our X-VLA model on three distinct real-world em- bodiments, each under specific task setups, including simple manipulation, dexterous manipulation, and fast adaptation experiments using PEFT techniques. See Appendix J for for details.
hour-comppnne comparable to the closed-source To To-folding model (Black et al., 2025a), which is presum- ably trained on substantially larger and higher-quality datasets. For fair comparison, we finetuned wo-base and ACT model on Soft-Fold, but it failed to match the throughput of X-VIA-0.99 under- scoring the strong dexterous manipulation capabilities of our model. Additional qualitative results provided in Appendix F and showcased in web demos: website.
Parameter efficient finetuning (PEFT) experiments. evaluate whether the pretrained X-VLA- 0.9B backbone encodes embodiment-agnosio features and can be efficiently adapted to settings, we adopt PEFT techniques such as Low-Rank Adaptation (LoRA) (Hu et al., 2022). We test adapta- tion three benchmarks: Libero, Himpler-Widww> X, and cloth-pick task AIRBOT, real-world embodiment unseen during pretraining. Tab. 3 and Tab. 8 show that with only 9M tunable param- be steered into strong domain-specializzd
eters (about 1% of the full model), the backbone model, achieving 93% and 54% success rates on Libero and Simpler-WidowX benchmarks, respec- tively. These scores are comparable fully finetuned models, e.g., To (Black et al., 2025a) achieve 94.2% and 55.7% on Libero and Simpler- WidowX respectively.
5.3 IN-DEPTH ANALYSIS
We further demystify the effects of soft prompts through both qualitative and quantitative results, examining whether the proposed soft-prompt mechanism can effectively absorb meaningful domain- specific knowledge from heterogeneous datasets.
Qualitative experiments. We visualize the soft prompts learned after pretraining the mixed data recipe (Fig. 3) using T-SNE (Maaten & Hinton, 2008). Each dot in Fig. corresponds to a token from the soft prompt sequence of specific data source. Since each data source includes multiple tokens

---

Published as a conference paper at ICLR 2026
T-SNE vieuelization of eoft prompts
Prompt Comparison Sidooe
Sigglo arm view
Methods
3u
Ours-Lora
50-
Franka cluster
#Faram
imnt
40
Franke (1 wee?
Lihero-Sparial
96.8
95.8
0.4
UK wine)
vien-right)
Libero-Object
98.8
96.3 + 0.3
20
Nocy Fromot
Libero-Goal
95.8
95.2
Franka 2 Vilss 2 @imnnaal view
Doul-hattaa
Agg lex view!
20
Agibor Pronept
Prompe
Frompt (two stepl
Simpler- WidowX
Libero- 85.2
55.7
83.7 1 0.5
54.2
S0K
100k
Training 130K
200c
Table 3: PEFT performance com-
Figure 9: T-SNE visualization of soft prompts on 7 data sources.
Figure 10: Comparison of dif- ferent prompts on PEFT.
parison across benchmarks.
to encode embodiment heterogeneity, jointly visualizing all tokens across embodiments intuitively reflects both intra- and inter- eide-eembdimment relationships. Fig. 9 reveals that the prompts form well- structured clusters that align closely with different hardware configurations, indicating that they successfully capture embodiment-speciff information. More excitingly, the two Franka setups (with left and right views) derived from Droid data are intermingled rather than separated, as they only differ in their designated main view. This observation suggests that soft prompts do not merely partition data sources in a brute-force manner but instead leverage cross-embodiment similarities.
Quantitative experiments. Further, we evaluate how pretrained soft prompts facilitate efficient adaptation to WidowX, an single-arm robot unseen in pretraining. We conduct PEFT experiments on Simpler, comparing four settings: (I) randomly initialized soft prompts kept frozen, (2) pre- trained prompts from dual-arm platform (e.g.. AgiBot) kept frozen. (3) pretrained prompts from another single-arm platform (e.g., UR5) kept frozen, and (4) soft prompts adapted with two-step adaptation mechanism. In Fig. 10, it's no surprise that learned prompts converge faster and finally reach higher success rates, whereas random prompts lead to slower adaptation and degraded perfor- mance. However, it's good to see that the frozen pretrained prompts offer strong transfer benefits in early stage due to the partial similarity between URS and WidowX, although the inevitable domain gap limits the final performance. Also, the Agi prompts offer subpar benefits at the beginning of training compared to UR5 prompts, largely due to the substantial embodiment mismatch between single arm and bi-manual arms. This highlights promising avenue for cross-embodiment trans- fer: with pretraining on more diverse robotic platforms, soft prompts may enable sero-shot/few-shoo generalization by retrieving prompts aligned with the closest hardware setups.
6 CONCLUSION
In this paper, we introduce X-VLA, a generalist Vision-Language-Action framework capable of op- erateing across heterogeneous robotic platforms. Through a carefully designed training pipeline, adaptation methods, and enhanced data processing, largest model X- V-VL--0.9B achieves SOTA performance across a broad spectrum of benchmarks, setting new records with substantial gains over hundreds of evaluation setups. Remarkably, even with minimal tunable parameters, X-VLA-0.98 delivers results competitive with fully finetuned SOTA models. Importantly, empowered by Soft Prompt mechanism, X- VLA exhibits scalable training trends along all three axes including model size, data diversity and data volume without sighs of saturation even at our largest test configuration (0.9B parameters, 290K episodes, data sources). This highlights its potential for further scaling to larger models and datasets, paving the way toward more powerful VLA models. Limitations and future works are discussed Appendix C.
ACKNOWLEDGMENTS
This work was supported by funding from the National Key R&D Program of China (2022ZD0160001, Shanghai AI Lab, Wuxi Research Institute of Applied Technologies, Tsinghua University (Grant No. 20242001 (20), Beijing Academy of Artificial Intelligence (BAAI), and Hori- zon Robotics. We thank Wencong Zhang for the help robot maintenance, Yiming Meng for the help on surveying simulation benchmarks, and Yiming Chen for the help on data collection.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, lige Akkaya, Florencia Leoni Ale- man, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, al. Gpt-4 technical report. arXiv preprint arXiv: 08774, 2023.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, al. Qwen technical report. arXiv preprint arXiv:2309. 16609, 2023a.
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2306 00022238.22000 /2966, 2023b.
Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Rich Walke, Chelsea Finn, Aviral Ku- mar, and Sergey Levine. Zero-shot robotic manipulation with pre-trained image-editing diffusion models. In The Twelfth International Conference on Learning Representations, 2024.
Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Robert Equi, Chelsea Finn, Niccolo Fusai, Manuel Y. Galliker, Dibya Ghosh, Lachy Groom, Karol Hausman, brian ichter, Szymon Jakubezak, Tim Jones, Liyiming Ke, Devin LeBlanc, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Allen Z. Ren, Lucy Xiaoyang Shi, Laura Smith, Jost Tobias Springenberg, Kyle Stachowicz, James Tan- ner, Quan Vuong, Homer Walke, Anna Walling, Haohuan Wang, Lili Yu, and Ury Zhilinsky. (0.55 a vision-language-acttoo model with open-world generalization. In 9th Anmual Conference Robot Learning. 2025a.
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri. Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, and Ury Zhilinsky. no. A vision- language-action flow model for general robot control. In Robotics: Science and Systems, 2025b.
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, al. Rt-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.068// 2022.
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choroman- ski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al. Rt-2: Vision-language-un models transfer web knowledge to robotic control, arXiv preprint arXiv:2307.1581 2023.
Qingwen Bu, Jisong Cai, Li Chen, Xiuqi Cui, Yan Ding. Siyuan Feng, Shenyuan Gao, Xindong He, Xuan Hu, Xu Huang, et al. Agibot world colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems. arXiv preprint arXiv:2503.0660 2025.
Tianxing Chen, Zanxin Chen, Baijun Chen, Zijian Cai, Yibin Liu, Qiwei Liang, Zixuan Li. Xi- anliang Lin, Yiheng Ge, Zhenyu Gu, al. Robotwin 2.0: A scalable data generator and bench- mark with strong domain randomization for robust bimanual robotic manipulation. arXiv preprint arXiv:2506. 18088, 2025.
Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, Zehao Yu, Katrin Renz, and Andreas Geiger. Transfuser: Imitation with transformer-basee sensor fusion for autonomous driving. IEEE trans- actions pattern analysis and machine intelligence, 45(11):12878-122) 12895, 2022.
Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovie, Marco Pavone, al. Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking. Advances in Neural Information Processing Systems, 37:28706-287199 2024.
Ria Doshi, Homer Walke, Oier Mees, Sudeep Dasari, and Sergey Levine. Scaling cross-embodied learning: One policy for manipulation, navigation, locomotion and aviation. arXiv preprint arXiv:2408. 118/2 2024a.
11

---

Published as a conference paper at ICLR 2026
Ria Doshi, Homer Rich Walke, Oier Mees, Sudeep Dasari, and Sergey Levine. Scaling cross- embodied learning: One policy for manipulation, navigation, locomotion and aviation. In 8th Anmual Conference on Robot Learning, 2024b.
Danny Driess, Jost Tobias Springenberg, Brian Ichter, Lili Yu, Adrian Li-Bell, Karl Pertsch, Allen Z Ren, Homer Walke, Quan Vuong, Lucy Xiaoyang Shi, al. Knowledge insulating vision- language-action models: Train fast, run fast, generalize better. arXiv preprint arXiv:2505.23705, 2025.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference machine learning, 2024.
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine. One-shot visual imita- tion learning via meta-learning In Conference on robot learning. Pp. 357-368. PMLR, 2017.
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard Turner. Meta- learning probabilistic inference for prediction. In International Conference Learning Repre- sentations, 2019.
Kyle Beltran Hatch, Ashwin Balakrishna, Oier Mees, Suraj Nair, Seohong Park, Blake Wulfe, Masha Itkina, Benjamin Eysenbach, Sergey Levine, Thomas Kollar, et al. Ghil-glue: Hierarchical control with filtered subgoal images. In IEEE International Conference Robotics and Automation, 2025.
Edward Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang. Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022.
Yihan Hu, Jiazhi Yang. Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, et al. Planning-oriemte autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 17853-17862, 2023.
Zheyuan Hu, Robyn Wu, Naveen Enock, Jasmine Li, Riya Kadakia, Zackory Erickson, and Aviral Kumar. Rac: Robot learning for long- long-horizon tasks by scaling recovery and correction. arXiv preprint arXiv:2509.07953 2025.
Chi-Pin Huang. Yuch-Hua Wu, Min-Hung Chen, Yu-Chiang Frank Wang, and Fu-En Yang. Thinkact: Vision-languse acctinn reasoning via reinforeed visual latent planning, arXiv preprint arXiv:2507.16811 2025.
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shah- baz Khan. Maple: Multi-modal prompt learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 19113-19122, 2023.
Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty El- lis, et al. Droid: A large-scale in-the-wild robot manipulation dataset. arXiv preprint 2rXiv:2403.12944 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 latoo-llnguage-acttin model. In 8th Annual Conference on Robot Learning.
Moo Jin Kim, Chelsea Finn, and Percy Liang. Fine-tuning vision-languagg-action models: Opti- mizing speed and success. arXiv preprint arXiv:2502. 19645, 2025.
Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efffcient prompt tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Pro- cessing. Association for Computational Linguistics, 2021.
12

---

Published as a conference paper at ICLR 2026
Bo Li, Yuanhan Zhang. Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer. Transactions on Machine Learning Research.
Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint vrXiv:2408.033200 2024a.
Jianxiong Li, Jinliang Zheng, Yinan Zheng, Liyuan Mao, Xiao Hu, Sijie Cheng, Haoyi Niu, Jihao Liu, Yu Liu, Jingjing Liu, Ya-Qin Zhang, and Xianyuan Zhan. DecisionNCE: Embodied multi- modal representations via implicit preference learning. In Forty-first International Conference on Machine Learning. 2024b.
Jianxiong Li, Zhihao Wang, Jinliang Zheng, Xiaoai Zhou, Guanming Wang, Guanglu Song, Yu Liu, Jingjing Liu, Ya-Qin Zhang, Junzhi Yu, al. Robo-mutual: Robotic multimodal task specification via unimodal learning. In 2025 IEEE International Conference on Robotics and Automation (ICRA). pp. 4182 4189. IEEE, 2025a.
Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference Natural Language Processing (Volume 1: Long Papers), pp. 3882-459,, 2021.
Xuanlin Li, Kyle Hsu, Jiayuan Gu, Oier Mees, Karl Pertsch, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sich, Sean Kirmani, et al. Evaluating real-worid robot manipulation policies in simulation. In Conference on Robot Learning, pp. 3705-3728. PMLR, 2025b.
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 2025.
action decoding in vision-language-action policies. arXiv preprint arXiv:2508.
Fanqi Lin, Yingdong Hu, Pingyue Sheng, Chuan Wen, Jiacheng You, and Yang Gao. Data scaling laws in imitation learning for robotic manipulation. In The Thirteenth International Conference on Learning Representations, 2025.
Yaron Lipman, Ricky Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. Flow matching for generative modeling. In The Eleventh International Conference on Learning Repre- sentations, 2023.
Yaron Lipman, Marton Havasi, Peter Holderrieth, Neta Shaul, Matt Le, Brian Karrer, Ricky TQ Chen, David Lopez-Par Paz, Heli Ben-Hamu, and Itai Gat. Flow matching guide and code. arXiv preprint ar 2rXv:2411.006266 2024.
Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning. Advances in Neural Information Processing Systems, 36, 2024a.
Dongxiu Liu, Haoyi NIu, Zhihao Wang, Jinliang Zheng, Yinan Zheng, Zhonghong Ou, Jianming Hu, Jianxiong Li, and Xianyuan Zhan. Efficient robotic policy learning via latent space backward planning. In International Conference on Machine Learning, 2025a.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems, 36:34892-34911 2023a.
Jihao Liu, Jinliang Zheng, Yu Liu, and Hongsheng Li. Glid: Pre-training generalist encoder- decoder vision model. In Proceedings of the IEEE/CVF conference compuler vision and pat- tern recognition, pp. 22851 -22660, 2024b.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language pro- cessing. ACM computing surveys, 55(9):1-35, 2023b.
13

---

Published as a conference paper at ICLR 2026
Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu. RDT-1b: a diffusion foundation model for bimanual manipulation. The Thirteenth International Conference Learning Representations, 2025b.
Yajing Liu, Yuning Lu, Hao Liu, Yaozu An, Zhuoran Xu, Zhuokun Yao, Baofeng Zhang, Zhiwei Xiong, and Chenguang Gui. Hierarchical prompt learning for multi-task learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10888- 10898, June 2023c.
Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9NNv))2557--2605, 2008.
Oier Mees, Lukas Hermann, Erick Rosete-Beas, and Wolfram Burgard. Calvin: A benchmark for language-conditiones policy learning for long-horizon robot manipulation tasks. IEEE Robotics and Automation Letters, 7(3):7327-7334 2022.
Yao Mu, Qinglong Zhang, Mengkang Hu, Wenhai Wang, Mingyu Ding, Jun Jin, Bin Wang, Jifeng Dai, Yu Qiao, and Ping Luo. EmbodiedGPT: Vision-language pre-training via embodied chain of thought. In "Thirty-sevents Conference on Neural Information Processing Systems, 2023.
NVIDIA, Johan Bjorck, Nikita Cherniadev Fernando Castañeda, Xingye Da, Runyu Ding. Linxi "Jim" Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, Joel Jang, Zhenyu Jiang, Jan Kautz, Kaushil Kundalia, Lawrence Lao, Zhiqi Li, Zongyu Lin, Kevin Lin, Guilin Liu, Edith Llontop, Loie Magne, Ajay Mandlekar, Avnish Narayan, Soroush Nasiriany, Scott Reed, You Liang Tan, Guanzhi Wang, Zu Wang, Jing Wang, Qi Wang. Jiannan Xiang, Yuqi Xie, Yinzhen Xu, Zhenjia Xu, Seonghyeon Ye, Zhiding Yu, Ao Zhang, Hao Zhang, Yizhou Zhao, Ruijie Zheng, and Yuke Zhu. GROOT N1: An open foundation model for generalist humanoid robots. In ArXiv Preprint, March 2025.
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, et al. Octo: An open-source generalist robot policy. arXiv preprint 2405.:2405.12211 2024.
Abby O'Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, et al. Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration O. In 2024 JEEE International Conference Robotics and Automation (ICRA). pp. 6892-6900 IEEE, 2024.
Jinyoung Park, Juyeon Ko, and Hyunwoo Kim. Prompt learning via meta-regularivation. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pp. 26940- 26950, 2024.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp. $195-4205, 2023.
Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. Fast: Efficient action tokenization for vision-language-action models. arXiv preprint a7X1::2501.09747, 2025.
Hai Pham, Young Jin Kim, Subhabrata Mukherjee, David P Woodruff, Barnabas Poczos, and Hany Hassan Awadalla. Task-based moe for multitask multilingual machine translation. In Pro- ceedings of the 3rd Workshop on Multi-lingual Representation Learning (MRL), pp. 164-172, 2023.
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 preprint arXi::2501.15830 2025.
Moritz Reuss, Jyothish Pari, Pulkit Agrawal, and Rudolf Lioutikov. policies with mixture of expert denoisers for multitask learning. arXiv preprint arXiv:2412. 12953,, 2024.
Efficient diffusion transformer
14

---

Published as a conference paper at ICLR 2026
Moritz Reuss, Hongyi Zhou, Marcel Rühle, Ömer Erding Yagmurlu, Fabian Otto, and Rudolf Li- outikov. FLOWER: Democratizing generalist robot policies with efficient vision-language-actioo action flow policies. In 7th Robot Learning Workshop: Towards Robots with Human- Level Abilities, 2025.
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. A reduction of imitation learning and struc- tured prediction to no-regret online learning. In Proceedings of the fourteenth international con- ference on artificial intelligence and statistics, pp. 627-6336 JMLR Workshop and Conference Proceedings, 2011.
Hao Shi, Bin Xie, Yingfei Liu, Lin Sun, Fengrong Liu, Tiancai Wang, Erjin Zhou, Haoqiang Fan, Xiangyu Zhang, and Gao Huang. Memoryvla: Perceptual-cognitiv memory in vision-languae action models for robotic manipulation. arXiv preprint arXiv:2508. (iti::200..193666 2025a.
Modi Shi, Li Chen, Jin Chen, Yuxiang Lu, Chiming Liu, Guanghui Ren, Ping Luo, Di Huang, Maoqing Yao, and Hongyang Li. Is diversity all you need for scalable robotic manipulation? arXiv preprint Stti::2507.06119 2025b.
Mustafa Shukor, Dana Aubakirova, Francesco Capuano, Pepijn Kooijmans, Steven Palma, Adil Zouitine, Michel Aractingi, Caroline Pascal, Martino Russi, Andres Marafioti, et al. Smolvla: A ssioo-languager arXiv:2506.01844 2025.
model for affordable and efficient robotics. arXiv preprint
Gemini Robotics Team, Saminda Abeyruwan, Joshua Ainslie, Jean-Baptiste Alayrac, Montser- Gonzalez Arenas, Travis Armstrong, Ashwin Balakrishna, Robert Baruch, Maria Bauza, Michiel Blokzijl, et al. Gemini robotics: Bringing ai into the physical world. arXiv preprint arXiv:2503.20020, 2025.
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre. Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al. Gemma: Open models based on gemini research and technology. arXiv preprint arXiv:2403.08295 2024.
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. In ICLR, 2025.
Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov. Lucas Beyer, Xiaohua Zhai, Thomas Un- terthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al. Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems, 34:24261- 24272, 2021.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302. 13971, 2023.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, E ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (eds.). Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017.
Homer Rich Walke, Kevin Black, Tony Z Zhao, Quan Vuong, Chongyi Zheng, Philippe Hansen- Estruch, Andre Wang He, Vivek Myers, Moo Jin Kim, Max Du, et al. Bridgedata v2: dataset for robot learning scale. In Conference on Robot Learning, pp. 1723-1736. PMLR, 2023.
Lean Wang, Huazuo Gao, Chenggang Zhao, Xu Sun, and Damai Dai. Aosssfar load balancing strategy for mixture-of-experts arXiv preprint arXiv:2408. 15664, 2024a.
Lirui Wang, Xinlei Chen, Jialiang Zhao, and Kaiming He. Scaling proprioceptive-vistte learning with heterogeneous pre-trained transformers. Advances neural information processing systems, 37: 37:124420-124455 2024b.
Yuqi Wang, Xinghang Li, Wenxuan Wang, Junbo Zhang, Yingyan Li. Yuntao Chen, Xinlong Wang, and Zhaoxiang Zhang. Unified vision- casion-lnggage--ace action model. arXiv preprint arXiv:2506. 19850, 2025a.
15

---

Published as a conference paper at ICLR 2026
Zhen Wang. Rameswar Panda, Leonid Karlinsky, Rogerio Feris, Huan Sun, and Yoon Kim. Mul- titask prompt tuning enables parameter-efficient transfer learning. In The Eleventh International Conference on Learning Representations, 2023.
Zhihao Wang, Jianxiong Li, Jinliang Zheng, Wencong Zhang, Dongxiu Liu, Yinan Zheng, Haoyi Niu. Junzhi Yu, and Xianyuan Zhan. Physiagent: An embodied agent framework in physical world. arXiv preprint arXiv:2509.24524 2025b.
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vin- cent Perot, Jennifer Dy. and Tomas Pfister. Learning to prompt for continual learning. In Pro- ceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp- 139-149, June 2022.
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. arXiv preprint arXiv:2312. 2023.
Kun Wu, Chengkai Hou, Jiaming Liu, Zhengping Che, Xiaozhu Ju, Zhuqin Yang, Meng Li, Yinuo Zhao, Zhiyuan Xu, Guang Yang, et al. Robomind: Benchmark on multi-embodiment intelligence normative data for robot manipulation. arXiv preprint arX1v:2412.13887, 2024.
Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, and Lu Yuan. Florence-2: Advancing a unified representation for a variety of vision tasks. Pro- ceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 4818- 4829, 2024.
Yifan Yang, Zhixiang Duan, Tianshi Xie, Fuyu Cao, Pinxi Shen, Peili Song, Piaopiao Jin, Guokang Sun, Shaoqing Xu, Yangwei You, et al. Fpc-vla: A vision-language-action framework with supervisor for failure prediction and correction. arXiv preprint arXiv:2509.0444 04008, 2025.
Seonghyeon Ye, Joel Jang, Byeongguk Jeon, Sejune Joo, Jianwei Yang. Baolin Peng. Ajay Man- dlekar, Reuben Tan, Yu-Wei Chao, Bill Yuchen Lin, et al. Latent action pretraining from videos. arXiv preprint 14 1158,, 2024.
Michal Zawalski, William Chen, Karl Pertsch, Oier Mees, Chelsea Finn, and Sergey Levine. Robotic control via embodied chain-of- of-thhugtttth reasoning. In 8th Annual Conference on Robot Learning, 2024.
Shiduo Zhang, Zhe Xu, Peiju Liu, Xiaopeng Yu, Yuan Li, Qinghui Gao, Zhaoye Fei, Zhangyue Yin, Zuxuan Wu, Yu-Gang Jiang, et al. Vlabench: A large-scale benchmark for language-conditionee robotics manipulation with long-horizon reasoning tasks. arXiv preprint 11X19.2412.18194, 2024a.
Zijian Zhang, Shuchang Liu, Jiaao Yu, Qingpeng Cai, Xiangyu Zhao, Chunxu Zhang, Ziru Liu, Qidong Liu, Hongwei Zhao, Lantao Hu, Peng Jiang, and Kun Gai. Moe: Multi-domain multi- task mixture-of experts recommendation framework. In SIGIR, pp. 893-902, 2024b.
Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning fine-grained bimanual manipulation with low-cost hardware. arXiv preprini xrXiv22004.33705 2023.
Haoyu Zhen, Xiaowen Qiu, Peihao Chen, Jincheng Yang, Xin Yan, Yilun Du, Yining Hong, and Chuang Gan. 3d-vla: A 3d vision-language-action generative world model. In International Conference on Machine Learning, pp. 61229-61245. PMLR, 2024.
Jinliang Zheng, Jianxiong Li, Sijie Cheng, Yinan Zheng, Jiaming Li, Jihao Liu, Yu Liu, Jingjing Liu, and Xianyuan Zhan. Instruction -uiioed visual masking. Advances in neural information processing systems, 2024a.
Jinliang Zheng, Jianxiong Li, Dongxiu Liu, Yinan Zheng, Zhihao Wang, Zhonghong Ou, Yu Liu, Jingjing Liu, Ya-Qin Zhang, and Xianyuan Zhan. Universal actions for enhanced embodied foun- dation models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp- 22508-22519 2025.
16

---

Published as a conference paper at ICLR 2026
Ruijie Zheng, Yongyuan Liang, Shuaiyi Huang, Jianfeng Gao, Hal Daumé III. Andrey Kolobov, Furong Huang, and Jianwei Yang. Tracevla: Visual trace prompting enhances spatial-temport awareness for generalist robotic policies. arXiv preprint arXh::2412.10345, 2024b.
Yi Zhou, Connelly Barnes, Jingwan Lu, Jimei Yang, and Hao Li. On the continuity of rotation representations in neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5745-5753, 2019.
17

---

Published as a conference paper at ICLR 2026
A LLM USAGE AND ETHICS STATEMENT
In this paper, we employed Large Language Models (LLMs) solely for polishing the writing. No parts of the technical content, experimental results, or conclusions were generated by LLMs.
A potential ethical concern is the use of large-scale pretraining data, which may contain privacy- sensitive information or embedded biases. To mitigate this, we use exclusively open-sourced robotics datasets (Bu al., 2025; Wu et al., 2024; Khazatsky et al., 2024; O'Neill et al., 2024), all of which have undergone peer review and are widely adopted in the research community. We believe this substantially reduces the risk of privacy violations or biased data influencing our results.
Nevertheless, we encourage future researchers to exercise caution when curating data for training large-scale robotics models, particularly by filtering privacy-sensitive content and addressing poten- tial biases to ensure responsible and fair deployments of embodied AI systems.
B RELATED WORK
Lsiiu--Languag-Acctin Models. Developing agents that interact with the physical world requires integrating three essential modalities: visual perception to understand the environment, language comprehension interpret task instructions, and action generation to produce executable control signals. Research on Assion-Language-ACe Action ( (VLA) models (Black et al., 2025a; NVIDIA et al., 2025; Zheng et al., 2025; Kim et al.) has focused on unifying these modalities embodied agents to perform complex tasks conditioned on natural language commands and visual observations. These models are typically built upon Vision- Language Models (VI.Ms) (Team al., 2024; Achiam et al., 2023; Li et al., 2024a; Xiao et al., 2024), which are pretrained on large-scale vasion-language corpora. By inheriting strong visual grounding and generalist reasoning capabil- ities from VLMs, VLA models achieve impressive results diverse manipulation tasks. More recently, researchers have recognized the inherent gap between general-purpose vision-language reasoning and embodied task requirements (Qu al., 2025; Black al., 2025a; Mu et al., 2023). To address this, various approaches have been explored, such as incorporating embodiment-speciff priors (e.g., 3D spatial grounding (Qu et al., 2025; Shi al., 2025a), instruction-followe following (Zheng et al., 2024a), historical reasoning (Shi et al., 2025a)) via modality injection (Qu et al., 2025), scal- ing up domain-relevant datasets (Black et al., 2025a: Li al., 2024b). adding extra supervision, or designing specialized models (Shi et al., 2025a). Nevertheless, due to the inherent limitations of cur- rent VLMs, these strategies often fall short of achieving the level of generalized reasoning required for embodied tasks with complex visual inputs. In this paper, we demonstrate that a simple yet effective modification of the input streams can better harness the generalization potential of VLMs, leading to significant performance gains.
enable
Heterogeneous Pretraining. Training on large-scale datasets has been a key factor of recent progress in embodied foundation models (Black et al., 2025b; Kim et al.). However, robotics data available for large-scale pretraining often exhibit strong heterogeneity, not only in action spaces but also in hardware setups (O'Neil et al., 2024). To address this, Wang et al. (2024b) proposed pretraining a standard Transformer on heterogeneous data mixtures with carefully designed archi- tectural modifications, demonstrating promising scaling behavior and transferability. More recently, researchers have observed that pretrained VLMs already possess strong generalization ability in handling diverse vision-language inputs across domains. Consequently, the focus has shifted to- ward resolving heterogeneity in action spaces (Zheng et al., 2025; NVIDIA et al., 2025; Zawalski al., 2024). Beyond manually reshaping action spaces (Liu et al., 2025b) or modifying model architectures to accommodate heterogencous action labels, several approaches have been proposed to align actions at the semantic level through latent action modeling or representation learning (Ye et al., 2024; Zheng et al., 2025; Li et al., 2024b; 2025a). Nevertheless, learning policies from het- erogeneous data sources requires more than aligning action labels, but embodiment-specifie and proprioceptive-aware reasoning, since variations in hardware setups directly affect how observa- tions map to actions. Simply feeding heterogeneous data into a shared backbone without explic- itly modeling these embediient--peccfic factors often leads to unstable training and poor cross- domain generalization. In this paper, we introduce a soft-prompt mechanism that explicitly absorbs embodimeen-speccffee variability while preserving shared backbone for general reasoning. By
18

---

Published as a conference paper at ICLR 2026
sociating each hardware configuration with learnable prompt embeddings, the model can flexibly capture domain -emaii--peecficc knowledge, thus enabling stable large-scale pretraining.
Soft Prompt Learning. The concept of soft prompts was originally introduced in the NLP commu- nity as a parameter-effiient alternative to full model finetuning. Instead of updating all parameters of pretrained model, small set of learnable embeddings are prepended to the input sequence and optimized for downstream tasks (Lester et al., 2021). This approach has proven highly effective in adapting large language models diverse tasks with minimal additional parameters, inspiring exten- sive research on prompt-based transfer learning across modalities (Li & Liang, 2021; Wang et al., 2022: Liu al., 2024b). Building on this foundation, soft prompts have been extended to multi- modal and multi-task learning settings (Liu et al., 2023c). When combined with the philosophy of meta-learning (Park et al., 2024; Gordon al., 2019), soft prompts can serve as lightweight carriers of domain- or task-specific information. By injecting learnable embeddings that guide the backbone without overwriting its pretrained representations, they provide flexible and scalable mechanism to handle domain heterogeneity (Wang et al., 2023). In this work, we adopt the soft prompt paradigm for robotics, where heterogeneity arises from embodiment differences such as hardware configu- rations and action spaces. We demonstrate that soft prompts effectively absorb embodiment- specific variability, enabling the backbone to focus on learning an embodiment-agnosté generalist policy.
C LIMITATIONS AND FUTURE WORKS
In this section, we discuss the limitations of our work and outline potential directions for future research.
Scaling X-VLA with broader data and model sizes. While X-VLA-0.9B achieves strong perfor- mance, its scale remains modest compared to large foundation models in the vision-language and language domains. This limitation stems primarily from computational constraints and the lim- ited availability of high-quality robotics data. Despite our efforts to collect and curate open-source datasets (Wu et al., 2024; O'Neill et al., 2024; Bu al., 2025), the diversity and scale of current robotics corpora still fall short of those in language or vision-language domains. Scaling X-VLA larger capacities, either by expanding the backbone or leveraging stronger pretrained VLMs. and training on broader, more diverse robotics datasets could further enhance generalization and robust- ness. Such extensions also raise open questions about the scaling laws of VLA models and how embodiment-specine variability interacts with model capacity.
Enhancing supervision signals for large-scale robotics pretraining. Despite our efforts to miti- gate heterogeneity across data sources and to align action spaces for generalized knowledge learning, the supervision provided by low-dimensional action labels remains inherently limited in information content. These labels, while essential for direct control, capture only narrow view of the underlying task structure and often fail to convey higher-Ievel reasoning, intent, or multi-step dependencies. In this work, we show that a simple temporal downsampling strategy can help abstract action intentions and thereby facilitate more efficient pretraining. However, such hcuristics only partially address the problem, as they do not fundamentally enrich the supervision. Future directions include incorporat- ing richer supervisory signals such as 3D spatial reasoning cues, physical dynamics or intermediate subgoal annotations. Another promising avenue is leveraging self-supervised objectives from raw input streams to complement sparse action labels, thereby enhancing representation learning and improving scalability in heterogeneous, real-world robotics settings.
Towards a generalist model seamlessly deployed to downstream tasks. Our X-VLA demon- strates superior performance across various downstream tasks, showing strong adaptability under fine-tuning and efficient specialization. However, realizing the vision of a truly generalist embodied model that can be seamlessly deployed to arbitrary downstream tasks without additional engineering or retraining remains an open challenge. Currently, deployment still relies on embodiment-specine adaptation, typically involving the collection of a small number of demonstrations for post-training. While these strategies are lightweight compared to full retraining, they nonetheless introduce over- head and prevent the model from serving as a true plug-and-plas solution in real-world applica- tions. Moreover, the dependence on embodiment-spenne specific data becomes problematic when scaling to platforms where high-quality demonstrations are scarce, expensive, or risky to collect. Future research should therefore focus approaches that move closer seamless deployment. Based
19

---

Published as a conference paper at ICLR 2026
on the empirical finding in this paper, a promising direction include exploring unified embodiment representations: incorporating explicit embodiment-agnoss abstractions (e.g., universal kinematic descriptors, physics-informec priors) to reduce reliance task-specific adaptation.
D MORE RESULTS
In this section, present additional results that highlight the strengths of approach. Specif- ically, we report: (1) comparisons between our model and alternative architectural designs, (2)
evaluations under cross-embodiment joint training. (3) evaluations in data-constrained settings, (4) ablation study on prediction window during pretraining and (5) ablation study on parameter-efficient finetuning methods
Libero-Long
Simple--WidowX
Calvin
DIT
MM-DiT
StySe
Ours
Singlo-domain FT Multi-domain FT
97.6
96.0
4.42
98.1
Validation Error
0.077
0.140
0.056
0.041
Table Joint adaptation to multiple embodiments. Multi-domain finetuning achieves performance com- parable to, and in some cases exceeding, single- domain finetuning, demonstrating the scalability of X- VLA to heterogeneous deployment settings.
Table 4: Comparison of backbone architectures on validation error. X- E-V achieves the lowest error while maintaining stable training.
D.1 ALTERNATIVE ARCHITECTURAL DESIGNS
In this section, present additional results from alternative architectural designs explored during development. While our final model adopts the X-VLA pipeline. we also implemented several com- monly used backbone architectures for comparison. These baselines were evaluated under identical experimental settings, consistent with the preliminary experiments described in Appendix I.
Standard DiT Decoder. direct application of the Diffusion Transformer (DiT) decoder (Peebles & Xie, 2023) that generates actions conditioned on multimodal features extracted by pretrained vasion-language encoders. This is the most straightforward extension of DiT to embodied settings.
Standard MM-DiT Decoder. A multimodal variant of DIT that allocates separate parameters for different input modalities and integrates them through attention (Esser al., 2024). We isolate the action modality from visual-language inputs. Although this design attempts to reduce the semantic gap across modalities, often destabilizes training and leads to inferior results on heterogeneous datasets and downstream adaptation.
StySe Decoder. Following (Black et al., 2025b), this design employs a parallel MLP-Mixer (Tol- stikhin et al., 2021) action module alongside a pretrained VLM for laniuae process- ing. This leverages the compact nature action inputs, which can be effectively represented with dense feed-forware networks, but comes the cost of added architectural complexity.
The comparative results across these backbones are summarized in Tab. 4, where our X- VLA consis- tently achieves the best validation performance while maintaining stable optimization dynamics.
D.2 POTENTIAL BUILD CROSS- COOSSEMBODIMENT GENERALIZED POLICY
Empowered by soft prompts, X- VLA enables efficient and stable training on heterogeneous datasets, effectively absorbing domain variations and fostering embodiment embodiment-agnnssic policy learning, Build- ing on this capability, we show that X-VLA can be adapted not only to single novel embodiment but also to multiple embodiments simultaneously through joint finetuning on demonstrations from di- conduct joint finetuning experiments
verse data sources. Concretely, datasets including Libero, BridgeData, and aalvin-ABC. which include two distinct embodiments and three hardware setups for both data collection and deployment. After joint finetuning using the same training recipe as other finetuning experiments in Appendix H, we report the results in Table 5.
mixture of downstream
Table shows the multi-doman adaptation results. X-VLA maintains consistently strong perfor- mance across all evaluated embodiments when adapted jointly, demonstrating its ability to scale beyond single-domain specialization. Interestingly, joint adaptation not only preserves performance
20

---

Published as a conference paper at ICLR 2026
within each domain but in some even improves success rates compared to single-domain fine- tuning, suggesting positive cross domain transfer. This indicates that the soft-prompt mechanism not only absorbs embodiment- sppeciffi variations but also enables complementary knowledge shar- ing across heterogeneous embodiments.
D.3 DATA-EFFICIENT ADAPTATION
# demos
Libero-Spatial
Libero-Object
Libero-Goal
Libero-Long
Libero-Avg
50 (Full & Default)
96.6
95.4
84.2
92.8
10
95.2
94.2
93.6
81.5
91.1
Table Data-efficient adaptation performance PEFT finetuned X- VLA-0.0 on Libero under limited demon- strations. Even with only 10 demonstrations, the model maintains strong performance.
In this section, we investigate whether the learned embodiment-agnostii backbone can be efficiently adapted novel embodiments under limited supervision. To this end, we finetune X-VLA-0.99 in PEFT setup on Libero-Gers Goal using only a small number of demonstrations, As shown in Table 6, the model achieves a 92.8% success rate with 50 demonstrations, and remarkably still retains strong 91.1% success rate with only 10 demonstrations. These results highlight the data efficiency of our two-step adaptation procedure, showing that the pretrained backbone, together with soft prompts, serves strong prior that enables effective specialization even under extreme data scarcity.
D.4 ABLATION STUDY ON THE PREDICTION WINDOW DURING PRETRAINING
The prediction window for pretraining on the curated datasets is selected through a careful grid search under small-scale preliminary setup. Specifically, pretrained 0.3B version of X-VLA (Florence- base and 100M self attention block) on our curated data recipe for 100K iterations and evaluated the resulting model via PEFT on Simpler-WidowX. since validation loss under different prediction targets are not directly comparable. As shown in Tab. 7, a 4-second window emerges as reasonable choice: it captures the intended action trajectory clearly while retaining sufficient future dynamics without excessive loss of detail.
prediction window
Success Rate
Param
Success Rate
Is
0
Prompt only (32K)
0
2s
8.3
+ Linear head (70K)
8.3
4s
29.16
LoRA (9M)
54.2
8s
27.08
Unfreeze last layer (25M)
68.9
Table 7: Ablation study on the prediction window during pretraining.
Table 8: Ablation study on parameter-efficient finetuning methods.
D.5 ABLATION STUDY ON DARAMETER-EFFICIENT FINETUNING METHODS
In addition to our standard PEFT approach, which combines two-phase soft-prompt tuning with LoRA finetuning, conducted additional experiment using standard prompt tuming, where also unfroze several last layers of the backbone to increase the number of learnable parameters and examine the effect of tunable parameter count. The results of this study reported in Tab. 8. These findings indicate that the number tunable parameters plays an important role in effectively adapting X-VLA specific domains.
E FAILURE ATTEMPTS FOR ABSORBING HETEROGENEITY
The core motivation of this paper is to explore strategies for mitigating heterogeneity across mixed data sources and to develop a generalist, ambodiment-agnosse policy. Inspired by the philosophy of meta-learning (Gordon al., 2019), initially approached this problem from the perspective of heterogeneous parameter learning. Concretely, we assigned distinct parameter sets for each domain, with the expectation that these domain-specific parameters could absorb domain variations while the
21

---

Published as a conference paper at ICLR 2026
Soft-FOLD
The first open-source high-quality cloth-folding dataset
Stage I: Smooth-out
Stage II: Fold
Carefully Designed Folding Pipeline: Daggle-Style Data Collection
1.2K episode
2M samples
24H Time Cost
Agilex
M
XL
XXL
Varied Cloth Color and Size
Figure 11: The illustration of our proposed Soft-Fold datasets.
shared backbone distilled generalizable knowledge across domains. Ultimately, we found that our proposed soft-proe prompttomppttttm mechanism provides effective solution to this challenge. In this section, present two of our unsuccessful attempts, with the aim of highlighting practical pitfalls and inspiring future work in this direction.
Heterogeneous Low-rank Adapter. Beyond soft prompts, we explored the integration of other parameter-efficient learning methods into heterogeneous pretraining. Specifically, experimented with Low-Rank Adaptation (LoRA)-style modules (Hu et al., 2022), where domain-specific adapters were introduced in parallel with the shared backbone. Our intuition was that these adapters could capture embodiment--pecific variations with efficient parameterization, and meanwhile the main backbone encodes embodiment-agnme aggnostic features. However, we observed that the additional adapters often conflicted with the optimization dynamics of the backbone, leading to instability and degraded generalization across domains.
Heterogeneity-guided MoE framework. We also experimented with mixture-of-ext (MoE) approach, which has been widely used for scaling model capacity while controlling inference cost. MoE's sparse activation mechanism (?) has proven effective in multi-task learning (Pham et al., 2023), cross-domain learning (Zhang et al., 2024b), and multi-modal robotics behavior cloning (Reuss et al., 2024). Motivated by these successes, we designed a heterogeneity-guidee routing strategy that aimed to activate experts based on embodiment-speccife cues. Despite its theo- retical appeal, we found that the router tended to collapse, consistently routing most inputs only few experts while leaving others underutilized, leading to wasted capacity and only marginal perfor- mance gains. To mitigate this, give another try introduce load-balancing regularization (Wang et al., 2024a), but the resulting rapid switching across experts often destabilized optimization and degraded overall training dynamics.
F SOFT-FOLD: SUPERIOR DEXTEROUS MANIPULATION MODEL WITH A HIGH-QUALITY CLOTH FOLDING DATASET
We provide qualitative results about our finetuned dexterous manipulation model from the pretrained X- VLA-0.98 and introduce a high-quality cloth folding dataset: Foft-FOLD, as illustrated in Fig 11.
Demonstration collecting strategy. Humans fold clothes casually and quickly, often using wide variety of methods in seemingly random manner. However, this variability poses significant challenges for robotic policy learning, since different folding strategies often correspond to distinct
22

---

Published as a conference paper at ICLR 2026
Figure 12: The folding progress of X-VI-A-0.9B.
behavioral modes, and not all strategies are equally suitable for training. To reduce the inconsis- tency in human demonstrations, we decompose the folding task into two stages: (1) smoothing out the cloth from a highly disordered state, and (2) folding the smoothed cloth neatly. We find that the first stage is particularly challenging, as the disordered cloth exhibits highly random dynamics, requiring the policy to capture universal strategy for unfolding. To address this, we collect demon- strations for stage in a repetitive manner until meaningful keypoints, such as the two corners or two ends of the cloth emerge clearly. At that point, we employ swinging motions to complete the smoothing stage and then transition to stage II. This is critical for cloth folding, unstructured or randomly collected demonstrations in stage can entangle policies in inconsistent behaviors, leading to unstable learning dynamics and hindering progression stage II. For stage II, the data collection becomes far more easier, as the cloth behaves less randomly after smooth-out. On average, one full folding episode takes about 1.5 minutes, with hour of collection yielding 20-25 episodes, in- cluding time for resetting and discarding failed attempts. The final dataset includes 1,200 episodes, as shown in Fig. 11.
DAgger-style data collection. To train long-horizon dexterous tasks such as cloth folding with limited episodes, find it essential adopt DAgger-style data collection strategy (Ross al., 2011), practice also noted by Hu et al. (2025). Concretely, we train ACT (Zhao et al.. 2023) after every 100 collected episodes, identify its failure modes, and then collect targeted demonstrations to address these failures. This iterative refinement enables us to achieve cloth-folding performance comparable to that of closed-soune -osere-ouuce models that are likely trained on substantially larger datasets, using only 1,200 episodes.
Qualitative results of X-VLA-0.9B. Here, we visualize a complete folding progress of our X-VLA- 0.9B in Fig. 12. One complete folding covers diverse skills, such as the simple Local zat on, Pick, Place and high-dynamical Swing ng motion, demonstrating the challenging of the cloth- folding tasks.
G PRETRAINING DETAILS
The pretraining of X-VIA-0098 9B was carried out on 64 NVIDIA A100 GPUs with global batch size of 1024, costing approximately 4 days. The training followed a carefully tuned recipe to ensure stability and efficient convergence across heterogeneous datasets.
Tab. 9 summarizes the core hyperparameters used during pretraining. We adopt the AdamW op- timizer with momentum parameters 31 = 0.9 and Bg 0.95, a learning rate of 10-4 and weight decay of 0.01. Training was performed for 200K iterations with mixed -reecison (bfloat16) For visual inputs, images are resized to 224 224 and augmented with mild perturbations using ColorJiterr to improve generalization.
In addition to the optimizer configuration, critical aspect of pretraining is balancing the het- erogeneous datasets. Since different sources vary greatly in both scale and quality, we adopt weighted sampling strategy combined with a carefully designed shuffling pipeline. As highlighted in Sec 1, this includes cross-domain and cross-trajectory shuffling, ensuring that the model is consistently exposed to diverse and balanced mixture of samples at every iteration. We find this design crucial for stabilizing optimization and preventing domain overfitting during large-scale pre- training. Tab. 10 summarizes the data sources, available trajectories, and the sampling weights applied.
23

---

Published as a conference paper at ICLR 2026
Validation set construction. We conduct open-loop validation experiments to monitor pretraining convergence and ensure fair comparisons across different architectures and methods. To guarantee that the validation loss serves clear and reliable proxy for downstream task performance, carefully construct dedicated validation set. Specifically, we sample trajectories from AGIBOT: beta (Bu al., 2025) that excluded from the training split, allowing to better evaluate cross- embodiment knowledge sharing and generalization. The validation set spans 189 tasks, with three trajectories sampled per task. For evaluation, we report the average & error between the predicted and ground-truti trajectories.
Configuration
Value
Data
Num. traj
Sampling weight
Optimizer
AdamW 1024
AGIBOT
141K
0.4
Batch size
Droid-Left
45K
0.15
Learning rate
10 0.01
Droid-Left
45K
0.15
Weight decay
Optimizer momentum
82,02 0.9, 0.95
SahoMMnnd-Frankee
19K
0.1
Training iterations
200K
RoboMind Dual Franka
Dual-Franka
2K
0.03
Model precision
b6
RoboMind-UR
25K
0.1
Image Resize
224x224
RoboMind-Agin
11K
0.07
Image Augmentation
ColorJitte(0.22, 0.2, 0.2, 0)
Table 10: Sampling weights for heterogeneous data sources during pretraining.
Table Hyperparameters for pretraining.
H FINETUNING DETAILS
In this section, we provide additional training details for the adaptation experiments. Unless oth- erwise specified, the optimizer settings (AdamW with 31 = 0.9, B2 0.95), weight decay (0.01), model precision (bfloat16), and learning rate (1 10 are kept consistent with the pretraining stage. All models are adapted using our proposed two-step procedure: during the first 1,000 itera- tions, only the soft prompts and action heads are updated while other parameters remain frozen; this is followed by 1,000-iteration warm-up phase that gradually restores the learning rate to its default value for joint training.
Table 11 summarizes the benchmark-specific hyperparameters. For clarity, Abs EEF denotes the absolute end-effector position control interface, while Re1 XYZ + Abs Rotation refers to rel- ative Cartesian translation combined with absolute rotation. All rotations are parameterized using the 6D representation, and the gripper state binarized and predicted via sigmoid activation. To maximize knowledge transfer from the pretrained backbone, we adopt aligned action representations (Abs ЕEF) across most downstream benchmarks. However, in the Simpler-Google benchmark, where the camera setup is deliberately altered test robustness against visual variation, adopt the Rel XYZ Abs Rotation control interface due to the sensitivity of absolute parameteri- zations domain shifts in perception.
Benchmark
Control Interface
Batch Size
Training Steps
Data Augmentation
CALVIN-ABC LIBERO
Abs EEF Abs EEF
128
60K
Colorlitter
128
60K
Ro00
Abs EEF
128
60K
Colorlitter
VLA-Bench
Abs EEF
128
60K
ColorJitter
BatdgeData
Abs EEF
128
60K
Colorlitter
FactalData
Rel XYZ + Abs Rotation
256
50K
RandomResizeCrrpp Colorlitter
SoftFold
Abs EEF
256
400K
Colorlitter
PEFT experiments
Abs EEF
128
40K
Colorlitter
Table 11: Finetuning hyperparameters for each downstream benchmark. Settings follow pretraining defaults
unless otherwise specified.
I TRAINING DETAILS FOR PRELIMINARY EXPERIMENTS
In this section, we provide additional details on the preliminary experiments. We adopt Florence- Base (Xiao et al., 2024) as the vision-language encoder and configure the backbone standard
24

---

Published as a conference paper at ICLR 2026
DiT-Base (12 Transformer layers, hidden size 768, with AdaLN conditioning) to compa- rability. Training is conducted on the curated heterogeneous data mixture using 8 NVIDIA A100 GPUs with global batch size of 256 for 200K iterations. Unless otherwise specified, all remaining settings (optimizer, weight decay, augmentation, and shuffling strategy) are kept consistent with the pretraining setup described in Section G. Following we provide more implementation details about baseline methods.
HPT-sttye Methods. Following (Wang et al., 2024b), we implement cross-atiention-basee resam- pler that maps domain-specific observations into shared representation space before feeding them into the DiT decoder. Each domain is assigned its own resampler and dedicated action head, while the core Transformer backbone remains shared across all domains. This design aims to mitigate observation heterogeneity while keeping the reasoning backbone general.
Language Prompts. In this setting, we provide embodiment-aware textual descriptions that encode hardware configurations and camera setups for each domain. These descriptions are concatenated with the task instruction and processed by the Florence- encoder, enabling the model to ex- plicitly attend to embodinent-speccfee variations. Table 12 lists the language prompt templates used across domains.
Domain
Language Prompts
RoboMind-Frund Franka
Embodiment: Single Franka, Camera Setup: Top View, Freq: 30Hz
RoboMind-UR
Embodiment: Single UR, Camera Setup: TTop View, Freq: 30Hz
Droid-Left
Embodiment: Single Franka, Camera Setup: Left View Wrist View, Freq: 15Hz
Droid-Right
Embodiment: Single Franka, Camera Setup: Right View / Wrist View, Freq: 15Hz
AGIBOT
Embodiment: AGIBOT, Camera Setup: Head View Wrist View, Freq: 30Hz
RoboMind-Agilex
Embodiment: AgileX, Camera Setup: Head View Wrist View, Freq: 30Hz
RohoMiind-Dull-Frrmm
Emhodiment: Dual Franka, Camera Setup: Front View / Wrist View, Freq: 30Hz
Table 12: Language prompts designed to provide embodiment- and camera-specific descriptions for each do- main the preliminary experiments.
J EVALUATION DETAILS IN REAL- WORLD EXPERIMENTS
In this section, we provide detailed descriptions of our real-world evaluation setups. We adapt X- VLA-0.9B to three distinct robotic embodiments, each selected to validate different aspects of the model's adaptability:
WidowX for pick-an and--ladeppeeeee experiments. X-VLA-0.99 9B finetuned on BridgeData directly deployed to evaluate its ability to perform robust manipulation compact platform. We conduct compre- hensive evaluations to assess both manipulation performance and language-instrus instruction following in real-world settings, illustrated in Fig 13 and each task is evaluated for 10 times.
Visual Generalization
Motion Generalization
Physical Generalization
Somantic Generalization
PigZ ant inn pat (w] dumeec
yellow into por twi clutte)
looght changs)
Flip the poc upright
Move drying
Figure 13: Illustration of tasks used in the WidowX pick-and-place experiments. The selected tasks evaluate different aspects of vitiaaiizaiio-iisaaal Motion, Physical, and Semantic-following the setup in Open- VLA (Kim et al.).
AgileX for dexterous manipulation tasks. As discussed in Appendix F, this setup is designed to test dexterous, fine-grained control bi-manual platform equipped with wrist-mounted cameras.
AIRBOT for parameter-efffcien finetuning experiments. AIRBOT is unseen during pretraining, We specifically collect only 200 demonstrations for cloth-picking task, making it challenging low-
25

---

Published as a conference paper at ICLR 2026
resource setting. This experiment highlights the adaptability of our two-step adaptation procedure under strict data and resource constraints.
Fig. 14 shows the hardware setups for these experiments. Each embodiment is equipped with distinct camera setups, enabling us to construct heterogeneous deployment environment for validation.
Top view
Wrist view
Left side view
Right wrist view
Left wrist view
(a) WidowX
[b] AgileX
(c) AIRBOT
Figure 14: Illustration of the hardware setups used in real-world experiments. We evaluate on three robotic emhodiments, including WidowX, Agilex, and AIRBOT, covering diverse camera configurations and task mains to form a heterogeneous validation environment.
K TRAINING DETAILS OF BASELINES IN REAL- WORLD EXPERIMENTS
In this section, we provide the training details of the real-world baselines.
To in cloth-folding task is finetuned from the official base To model using the Soft-Fold dataset described in Appendix F. The model is trained with a total batch size of 32 across 4 A100 GPUs, requiring approximately 60 hours complete 150,000 gradient steps.
To in PEFT experiments is finetuned from the official base model using the official LoRA configuration. We apply LoRA with rank 16 and a 16 both the attention and FFN modules within the PaliGemma-2B VLM. For the action expert, we use rank 32 and a = 32. Training performed with a total batch size of 32 across A800 GPUs, taking approximately hours to complete 30,000 gradient steps.
ACT cloth-folding task is trained from scratch using the Soft-Fold dataset described in Ap- pendix F. The model is trained with a total batch size of 256 on 8A100 GPUs. Since the model capacity ACT is not high as large model such as X-VLA and No. we train ACT approximately IM gradient steps for better training.
L EVALUATION DETAILS ON AUTONOMOUS DRIVING SIMULATION BENCHMARK
We evaluate our method on the large-scale real-world autonomous driving benchmark NAVSIM (Dauner et al., 2024) using closed-loop assessment. Following the official evaluation protocol, we report the PDM score (higher indicates better performance), which aggregates five key metrics: NC (no-collision rate), DAC (drivable area compliance). 77C tiime-t--oollisoo safety), Comfort (acceleration/jerk constraints), and EP (ego progress). All methods are tested under the official closed-loop simulator, and results are averaged over the public test split, As an end-to-end VLA model, our method achieves superior performance over specialized methods designed for au- tonomous driving, with detailed scores reported in Table 13.
M EVALUATION DETAILS ON ROBOTICS SIMULATION
We report detailed scores for each simulation benchmark in Table 14-18.
26

---

Published as a conference paper at ICLR 2026
NAVSIM
Methods
NC
DAC
EP
TTC
PDMS
Transfuser (Chitta et al., 2022)
97.7
92.8
79.2
92.8
100.0
84.0
UniAD (Hu et al., 2023)
97.8
91.9
78.8
92.9
100.0
83,4
UniVLA (Wang et al., 2025a)
96.9
91.1
76.8
91.7
81.7
X-VLAA (Ours)
97.5
96.5
82.2
92.9
100.0
87.3
Table 13: Detailed results NAVSIM benchmark.
Simpler Visual Aggregation (Google Robot)
Visual Matching (Google Robot)
Visual Matching (WidowX Robot)
Coke 98.3
Near 97.1
Open 69.5
Put 56.5
Average 80.4
Coke 85.5
Near 79.8
Open 61.9
Average 75.7
Spoon
Carrot 91.7
Blocks 95.8
Eggplant 95.8
Average 95.8
75.7
Table Detailed results on Simpler benchmark.
Libero-Sputial
98.2
97.1
In Distribution
67.8
Libero-Object
98.6
92.6
Cruss Category
88.5
Libero
Libero-Gon
97.8
(ABC-D)
84.4
VLABench
Common Sense
48.2
Libero-Long
97.6
78.8
Semantic Instruction
Averuge
98.1
Average
4.43
Average
51.1
Table 15: Details on Libero.
Table 16: Details Calvin.
Table 17: Details on VLA Bench.
Twn-22
Task
Easy
Hard
Task
Easy
Hard
Task
Easy
Hard
Adjust Bottle
97.0
56.0
Open Microwave
85.0
$7.0
Place Object Stand
78.0
33.0
Beat Block Hammer
78.0
18.0
Pick Diverse Bottles
27.0
25.0
Place Phone Stand
80.0
9.00
Blocks Ranking RGB
Pick Dual Bottles
Shoe
70.0
Blocks Ranking Size
42.0
9.00
Place A2B Left
62.0
21.0
Press Stapler
70.0
13.0
Click Alarmelock
96.0
69.0
Place A2B Right
54.0
17.0
Put Bottles Dustbin
0.00
1.00
Click
61.0
Place Bread Basket
75.0
39.0
Put Object Cabinct
78.0
82.0
Dump Bin Bigbin
94.0
59.0
Place Bread Skillet
82.0
17.0
Rotate QRcode
78.0
52.0
Grab Roller
99.0
66.0
Place Burger Fries
98.0
47.0
Scan Object
60.0
44,0
Handover Bluck
27.0
30.0
Place Can Basket
58.0
18.0
Shake Horizontally
99.0
100.0
Handover Mic
100
38.0
Place Cans Plasticbox
100
85.0
Shake Bottle
99.0
99.0
Hanging Mug
Place Container Plate
Stack Blocks Three
22.0
Lift Pot
99.0
75.0
Place Dual Shocs
98.0
28.0
Stack Blocks Two
87.0
55.0
Move Can Pol
50.0
44.0
Place Empty Cup
98.0
34.0
Stack Bowls Three
80.0
42.0
Move Pillbottle
29.0
Place Fan
72.0
27.0
Stack Bowls Two
83.0
Move Playingeard Away
94.0
57.0
Place Mouse Pad
19.0
3.00
Stamp Seal
52.0
13.0
Move Stapler Pad
58.0
35.0
Place Object Basket
50.0
Turn Switch
40.0
13.0
Open Laptop
85.0
73.0
Place Object Scale
39.0
13.0
Average
70.0
39.0
Table 18: Detailed results on RoboTwin-220 benchmark.
27


Reviews:
[{"review_id": "1R2u91oZCq", "reviewer": "Reviewer_PCNL", "summary": "The paper proposes X-VLA policy for learning across different robot setups using soft prompts for each data source. The pipeline is trained with a flow-matching objective for action chunk generation. The training involves first pretraining on ~290k episodes across seven setups, then prompt warm-up with frozen backbone for new embodiment and finally joint PEFT (LoRA) that tunes ~1% of parameters. The evaluation shows strong performance across six simulation benchmarks and three real-robot platforms. The learned soft prompts cluster based on hardware in t-SNE plot.", "questions": "What does the prefix \"high-dimensional\" observation stream and \"low-dimensional\" proprioceptive–action stream mean? Can you clarify what is it in comparison to, like does it have more frames for history or multi-perspective viewing? How big are the \"low-dimensional\" proprioceptive–action vectors, and how do they differ with different embodiments?\n\nAre the results statistically significant? \n\nHow useful are the learned soft prompts? While the paper provides a t-SNE plot to show how embeddings cluster by hardware, it is unclear how important they are for reusing in a new instance of a task. For example, if a soft prompt embedding is used with another task same embodiment or another embodiment, what is the performance degradation on the task?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The idea of soft prompts is motivated well, and is shown to be better than the language templates and HPT-style projections. \nThe overall proposal seems a scalable solution, as it is based on standard transformer with shared params for encoders and validation errors suggest room to grow. \nThe paper explores how reduced LR leads to better performance for their experiments and report consistent gains in performance across the simulated benchmarks.", "weaknesses": "Prompts are queried by dataset/hardware ID; this assumes reliable domain labels during training and may risk overfitting to source IDs unless carefully regularized. There are no experimental evidence of zero-shot generalization to unseen tasks with known robots and unseen robots.\n\nWhile the experiment results has a wide coverage, the current focus is on tabletop tasks with focus on the end-effector movements, and do not require much reasoning of the differences in the embodiment. It is unclear if the proprioceptive states outputs are actually needed for the evaluation tasks.   \n\nThe results use PEFT with 1% tunable params that is promising for on-robot finetuning, but this has been compared on quite saturated Libero benchmark. The results reported have no error bars or confidence intervals.", "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": "8ojWNXilMM", "reviewer": "Reviewer_R55g", "summary": "The paper proposes X-VLA, a vision-language-action model that leverages \"soft prompts\" (learnable embeddings of heterogeneous hardware platforms) to effectively address heterogeneity across different robot platforms when learning multi-task policies. The method learns domain-specific soft prompt embeddings that encode individual embodiments, with a shared VLM backbone that utilizes the soft prompts to ultimately generate high-quality actions across multiple embodiments and tasks. The model is pretrained on 290K demonstrations from seven robot platforms that include both single-arm and dual-arm manipulation setups. Experimental evaluations show strong, state-of-the-art performance across the benchmarks, which include LIBERO, SIMPLER, CALVIN, and others.", "questions": "* How does X-VLA perform under OOD generalization settings?\n* When does X-VLA fail during deployment, and what do the failure modes look like qualitatively?\n* Can a soft prompt be used for zero-shot deployment on an unseen robot if the soft prompt was trained for a seen robot that is very similar to the new robot?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "* The paper conducts extensive and thorough experimental evaluation which includes six simulation benchmarks and three real-world robotic platforms. A variety of prior methods are compared against, including $\\pi_0$, OpenVLA, Octo, ACT, and several other VLAs (UniVLA, GR00T-N1, FLOWER, UniVLA, etc.).\n* The results across all settings are consistently strong, nearly outperforming all other prior methods and even matching $\\pi_0$ (3B parameters) with a PEFT version that use only 9M trainable parameters.\n* The experimental analysis on various components of X-VLA is comprehensive, with various ablations and a study on scaling behavior across model and dataset size. Further, the qualitative and quantitative discussions on the effects of the learned soft prompts are informative and support the claims that the author makes regarding the ability to encode heterogeneous morphologies in a shared structured latent space.\n* The paper also introduces the Soft-Fold bimanual cloth folding dataset with 1,200 demonstrations.\n* The uncut video on the project website showcasing two hours of t-shirt folding demonstrates impressive, robust performance of X-VLA across an extended period of time.\n* I appreciate the transparent discussion of failed attempts to address heterogeneity across mixed data sources presented in Appendix E. Reporting unsuccessful attempts may provide useful information to readers.", "weaknesses": "* Soft prompts, the core contribution of the paper, is borrowed from prior natural language processing research (Lester et al., 2021), which suggests limited technical novelty. While the application of soft prompts to vision-language-action models in robotics is novel, the paper reads more like an engineering project that obtained a strong model through various careful techniques employed during training.\n* Some important details are missing: How many tokens are used in the soft prompts, how is the number chosen, and how does performance vary with respect to the number of tokens used in the soft prompt? In addition, how is the temporal downsampling rate for pretraining chosen, and how does that affect policy performance? Further, what effect does the prompt warm-up phase discussed in Section 4.2.1 have on final performance? The paper would be strengthened by additional discussions addressing these questions.", "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": "8E1Jv8ZAmj", "reviewer": "Reviewer_F3Do", "summary": "This paper addresses the significant challenge of training a single, generalist Vision-Language-Action (VLA) model on large-scale, heterogeneous, and cross-embodiment robotic datasets. The authors propose **X-VLA**, a flow-matching-based VLA architecture. The core contribution is a **\"Soft Prompt\" mechanism**, which assigns a unique set of learnable embeddings to each distinct data source (i.e., hardware configuration). These prompts condition a standard Transformer backbone, enabling it to handle the heterogeneity arising from different embodiments while learning a generalist policy. The model is trained using a two-phase pretraining and domain adaptation strategy. The authors demonstrate that their 0.9B parameter model, X-VLA-0.9B, achieves new state-of-the-art (SOTA) performance across a comprehensive suite of six simulation benchmarks and three real-world robot platforms , and shows remarkable parameter-efficient finetuning (PEFT) capabilities.", "questions": "1.  The soft prompt is described as \"a set of learnable embeddings\" , and Figure 5 (center) explores \"Prompt Length\". Does this confirm that each \"soft prompt\" is a *sequence* of learnable tokens (e.g., a matrix of size $L \\times D_{model}$)? If so, what prompt length was used for the final X-VLA-0.9B model?\n\n2.  Regarding the T-SNE visualization in Figure 8: What do the individual dots represent? If the prompt is a sequence of tokens, does each dot represent one token from that prompt sequence, for its corresponding data source?\n\n3.  The two-step adaptation procedure  involves (1) prompt warm-up and (2) joint policy adaptation. The PEFT experiments (Table 3)  use LoRA. Is this LoRA applied *in addition* to the two-step prompt tuning? As a baseline, did the authors try *only* tuning the new soft prompt (while keeping the entire 0.9B backbone frozen), which would be a more standard \"prompt tuning\" PEFT approach?\n\n4.  In the architecture (Section 4.1) , the paper mentions disentangling streams, with a pretrained VLM for the main view/language and a \"shared vision backbone\" for auxiliary views. Is this \"shared vision backbone\" the *same* vision encoder from the VLM, or is it a separate vision encoder?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1.  **Clear Problem, Elegant Solution:** The paper tackles a critical and timely problem in large-scale robotics: how to effectively leverage diverse, heterogeneous datasets. The proposed \"Soft Prompt\" mechanism  is an elegant and simple solution that integrates cleanly into a standard Transformer architecture. This approach is more deeply integrated than using domain-specific action heads (Fig 2a)  and more flexible and scalable than relying on hand-crafted language prompts (Fig 2c).\n\n2.  **Exceptional Empirical Results:** This is the paper's most significant strength. The evaluation is both comprehensive and highly convincing.\n    * The model achieves SOTA results on an impressively broad set of **6 simulation benchmarks** (Libero, Simpler, VLABench, RoboTwin-2.0, Calvin, and NAVSIM). The performance gains shown in Table 2 are substantial (e.g., 95.8% on Simpler-WidowX vs. 71.9% prior SOTA; 87.3% on NAVSIM vs. 81.7% prior SOTA).\n    * The work is validated on **3 distinct real-world robotic platforms** (WidowX, Agilex, AIRBOT), demonstrating strong real-world applicability. This includes impressive performance on a challenging dexterous cloth-folding task.\n    * The introduction and planned release of the new **\"Soft-Fold\" dataset** for dexterous manipulation is a valuable contribution to the community.\n\n3.  **Strong Adaptation and Parameter Efficiency:** The parameter-efficient finetuning (PEFT) results are a key highlight. The ability to achieve performance comparable to a fully finetuned 3B parameter model ($\\pi_{0}$) while tuning only 9M parameters (1% of the 0.9B model) is a powerful demonstration of the model's capabilities. This strongly supports the claim that the backbone learns a truly \"embodiment-agnostic\" generalist policy.\n\n4.  **Thorough Analysis and Ablations:** The paper does an excellent job of justifying its design choices.\n    * Figure 4 provides a direct comparison against other methods for handling heterogeneity, showing that soft prompts lead to more stable training and lower validation error.\n    * Table 1 offers a clear ablation path, demonstrating the positive contribution of each key component (e.g., soft-prompt, scaling up, two-step adaptation).\n    * The scaling plots in Figure 5 are promising, showing consistent improvement with model size, data diversity, and data volume, with no sign of saturation.\n    * The T-SNE visualization in Figure 8 provides compelling qualitative evidence that the soft prompts are successfully capturing meaningful, embodiment-specific information.", "weaknesses": "1.  **Limited Methodological Novelty of Soft Prompts:** The core mechanism, \"Soft Prompting,\" is not a new invention. It is a well-established technique from the NLP community (e.g., Lester et al., 2021)  and has been used in multi-task and multi-domain learning , as the paper acknowledges. The novelty here lies in its *application* to the *cross-embodiment robotics* domain. While the *results* are novel and SOTA, the *methodological* leap is more of an effective and well-executed application rather than a new fundamental mechanism.\n\n2.  **Clarity on Soft Prompt Implementation:** Key details about the soft prompt's architecture are not fully clear in the main text. Figure 1 shows them feeding into the Transformer block, but the exact mechanism (e.g., concatenation, addition) is ambiguous. This is clarified in Appendix C / Figure 10, which shows the \"Soft Prompt\" tokens are concatenated with the multi-modal tokens and control tokens. Moving this crucial implementation detail from the appendix to the main methodology section would significantly improve the paper's clarity and self-containedness.\n\n3.  **Ambiguity in T-SNE Visualization:** The T-SNE plot in Figure 8  is a key piece of qualitative evidence, but it's not entirely clear what is being plotted. The legend lists 7 data sources , but there are many dots for each source. Given that Figure 5 (center) explores \"Prompt Length\", it's plausible that the prompt is a *sequence* of tokens. If so, does each dot in the T-SNE plot represent a single token from that sequence? Clarifying this is important for correct interpretation.", "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)