Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning
Authors: Ganlin Yang
Tianyi Zhang
Haoran Hao
Weiyun Wang
Yibin Liu
Dehui Wang
Guanzhou Chen
Zijian Cai
Junting Chen
Weijie Su
Wengang Zhou
Yu Qiao
Jifeng Dai
Jiangmiao Pang
Gen Luo
Wenhai Wang
Yao Mu
Zhi Hou
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-Language-Model
Vision-Language-Action-Model
Embodied Reasoning
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce Vlaser,  a Vision-Language-Action Model with synergistic embodied reasoning capability, which is a foundational vision-language model designed to integrate high-level reasoning with low-level control for embodied agents.

Abstract: While significant research has focused on developing embodied reasoning capabilities using Vision-Language Models (VLMs) or integrating advanced VLMs into Vision-Language-Action (VLA) models for end-to-end robot control, few studies directly address the critical gap between upstream VLM-based reasoning and downstream VLA policy learning. In this work, we take an initial step toward bridging embodied reasoning with VLA policy learning by introducing **Vlaser** - a **V**ision-**L**anguage-**A**ction Model with **s**ynergistic **e**mbodied **r**easoning capability, which is a foundational vision-language model designed to integrate high-level reasoning with low-level control for embodied agents. Built upon the high-quality Vlaser-6M dataset, Vlaser achieves state-of-the-art performance across a range of embodied reasoning benchmarks—including spatial reasoning, embodied grounding, embodied QA, and task planning.
Furthermore, we systematically examine how different VLM initializations affect supervised VLA fine-tuning, offering novel insights into mitigating the domain shift between internet-scale pre-training data and embodied-specific policy learning data. Based on these insights, our approach achieves state-of-the-art results on the WidowX benchmark and competitive performance on the Google Robot benchmark. We will open-source the model weights, data generation pipelines, and the full dataset to support future research.

Published as a conference paper at ICLR 2026
VLASER: VISION-LANGUAGE-ACTION MODEL WITH SYNERGISTIC EMBODIED REASONING
Ganlin Yang!.2, Tianyi Zhang Haoran Hao Weiyun Wang"., Yibin Liu Dehui Wang Guanzhou Chen Zijian Cai Junting C Weijie Su², Wengang Zhou', Yu Qiao² Jifeng Dai?.2, Jiangmiao Pang", Gen Luo", Wenhai Wang", Yao Mu Zhi Hou
Tianyi
'University of Science and Technology of China Shanghai AI Laboratory
Shhnnhai Jiao Tong University *Zhejiang University *Nanjing University FFudan University Shhennhenn University
"Tsinghua University NUS "Northeastern University
Project Page: Vlaser
ABSTRACT
While sigmificant research has focused on developing embodied reasoning capabili- ties using Vision-Language Models (VLMs) or integrating advanced VLMs into Assion-Language-Aciie (VLA) models for end-to-end robot control, few studies directly address the critical gap between upstream VL.M-based reasoning and down- stream VLA policy learning. In this work, we take an initial step toward bridging embodied reasoning with VLA policy learning by introducing Vlaser a Vision- Language-Action Model with synergistic embodied reasoning capability, which is a foundational vision- saiiun- model designed to integrate high-level reasoning with low-level control for embodied agents. Built upon the high-qualin Vlaser-6M dataset, Vlaser achieves state-of-the art performance across a range of embodied reasoning benchmarks-includinn bodied QA, and task planning. Furthermore, we systematically examine how different VLM initializations affect supervised VLA fine-tuning, offering novel insights into mitigating the domain shift between internet-scale pre-training data and embodied-specific policy learning data. Based these insights, our approach achieves state-of-the-as results on the WidowX benchmark and competitive perfor- mance the Google Robot benchmark. The code, model and data are available https: /tte:///gitte httggttttcccooo/o httpp:/:///tttuu..omm/o/e/eeeeee http://.....oooeeeeeaa//////eeae
spatial reasoning, embodied grounding, em-
INTRODUCTION
Embodied artificial intelligence (AI) (Chrisley 2003) aims to endow agents with the ability to perceive, understand, and act in the physical world. Achieving such intelligence requires not only accurate perception and language understanding but also embodied reasoning and effective control, which together define the paradigm of vision- lanoo-llngaag--attn (VLA) models. Developing foundation models that possess strong reasoning and control capabilities is therefore an important advancement toward general-purpone embodied AI.
In this context, vision-languag models (VLMs) (OpenAl 2023 Liu et al. 2023 Chen et al. 2024
Bai et al., [025 Team et al., (2023) emerge as natural candidates enhance embodied agents in perception generalization and reasoning ability. Following this paradigm, extensive embodied vision- language models (Azzolini et al. 2025 Team et al., 2025c) emerge from enhancing the key ability for embodied agent in grounding, planning, and spatial reasoning. Meanwhile, significant body of work extends vision-lanm laniuug models (VLMs) into vision-language-aatteo
models (VLAs) (Kim) Driess al. 2025) for robot control. While there some approaches (Intelligence et al. 2025 Driess et al.
et al. 2024 Intelligence al. 2025
2025) that demonstrate the effectiveness co- training with web data for the generalizzan inaerolizaiion in robot manipulation, it remains poorly understood which multi-modal data streams/abilities are most critical for improving downstream VLA models. In this paper, we aim construct Vlaser, embodied vision-language model that possesses strong
"qqual contribution. CCorespooning authors.

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Published as a conference paper at ICLR 2026
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Figure 1: Overall framework, capabilities, and evaluation of Vlaser. Top-left: Composition of the Vlaser-6M dataset, featuring multi-task embodied data-including QA, grounding, spatial reasoning. and planning-alowe with in-domain cnullaaion-suurced pairs. Top-right: LiDAR visualization illustrating the state-of- state-of-the-art the-art embodied reasoning capability of the Vlaser VLM. Bottom-left:
The pre-trained Vlaser VLM significantly accelcrates convergence in downstream Vision-Language
Action model (VLA) policy learning WidowX platform (Walke et al. (023a). Bottom-right: Successful closed-loop operation of an agent powered Vlaser within the SimplerEnv benchmark (Li
et al. 2024c).
embodied reasoning capabilities, and subsequently answer this question based on the corresponding Mision-languaag-actoo models.
Despite advancements in vision-language models (Chen et al. 2024 Bai et al. 2025) the capabilities of operating as an embodied agent remain severely constrained. In particular, navigation and traditional manipulation approaches rely heavily planning-boi based control (Huang et al.) 2022 Gasparetto et al. 2015 Zhang et al. 2018), which requires strong foundational ability in grounding and planning. Planning and Grounding cornerstones of the agents embodied in the physical world. Meanwhile, spatial understanding increasingly attracts the interest of the community addressing the spatial perception ability of VL.M. To this end, we firstly aim to introduce embodied vision- language model specifically enhanced for the aboved embodied reasoning capabilities. Specifically, we construct the Vlaser data engine, which enables the systematic construction of the Vlaser-6M dataset by curating, reorganizing, and annotating public datasets from the Internet. As illustrated in Figure T] the resulting dataset spans wide spectrum of embodied reasoning tasks-including general embodied QA, visual grounding, spatial intelligence, task planning, and in-domain simulation data. Leveraging this comprehensive data foundation, Vlaser achieves state-of-the of-the-ar- performance across variety of embodied reasoning benchmarks, demonstrating strong generalization in both open-loop inference and closed-loop control settings.
Existing Vision-Language-Action (VLA) models (Black et al. 2024 Cheng et al. 2024 Kim et al.) 2024; Intelligence et al., (0025) typically fine-tune pre-trained Vision-Language Models (VLMs) for robot control. However, the selection of an optimal VLM backbone one that accelerates convergence and improves success rates when used as initialization for end-to-end VLA policy learning, remains an mader-explored research problem. To address this gap, systematically investigate the VLM-to- VLA adaptation paradigm using our enhanced embodied vision- language model and associated data engine. Our experiments reveal important insight: although out-of-domain embodied reasoning
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textual tokens
action tokens
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Locats the lcoecil.
Visual Input
Task-specific prompts
(a) Multimodal Pretraining
(b) VLA Finetuning
Figure 2: An illustration of Vlaser architecture. Vlaser includes components and corresponding training phases: 1) the Multimodal Pretraining is for embodied reasoning enhancement based on the corresponding data engine; VLA training performed on the action expert module, which handles low-level control based on flow matching action generation.
data significantly improve upstream reasoning capabilities as measured by standard benchmarks, these gains may not translate directly or prominently downstream VLA performance. In contrast, in- domain data annotated directly on robot interaction datasets such as Open X-Embodiment (O'Neill al., 2024) proves substantially more effective in accelerating convergence and increasing task success rates during VLA fine-tuning. We believe this observation provides significant insights for future embodied vision- lsiinnlanguaae model construction: is urgent to shrink the domain gap between current embodied perception and reasoning benchmarks to the real-world embodied tasks, and thus facilitate closed-loop evaluation for the corresponding robot embodiment.
In summary, the principal contributions of Vlaser are as follows.
An open-source embodied vision-language model and dataset. We introduce Vlaser, an adaptable vision-lane languaee model that enhances Intern VL with embodied reasoning capabilities and end-to-end robot control. The full model weights, modular data generation pipeline, training and evaluation code, and the accompanying Vlaser-6M dataset will be made publicly available to support reproducibility and future research.
Systematic analysis of data effectiveness for VLA transfer. We conduct a thorough investigation into which types of Dision-langure pretraining data contribute most effectively to downstream Vision-Language-AAtiee (VLA) policy learning. Our findings offer practical insights for constructing task-aware data streams that bridge the gap between Internet-scale pretraining and embodied-speciffc fine-tuning.
State-of-tie -ftth--rt- performance across embodied benchmarks. Among models of comparable scale, Vlaser achieves top-tier results on comprehensive set of embodied reasoning benchmarks benchmaaks-sspaningg visual grounding, task planning, spatial reasoning, and simulation- based robot evaluation, demonstrat- ing its strong generalization and applicability both open-loop inference and closed-loop control scenarios.
METHOD
Vlaser aims to integrate embodied reasoning with end-to-end robot control for embodied agents, and identify the most crucial VLM data stream for VLA models. We first present the Vlaser structures in Section 2.]] Then, we illustrate the data engine in Section 2.2 Section 2.3 discusses the training recipe that includes embodied reasoning pretraining and vision-languuge-actinn finetuning.

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Published as a conference paper at ICLR 2026
2.1
MODEL STRUCTURE
The structure of Vlaser consists of two major components: the typical Hasgunn back- bone (Chen et al. 2024 Liu et al., 2023) and the action expert for low-level control, as shown in We illustrate the two components respectively in this section.
Figure 2
VLM Backbone Vision-language models (VLMs) are key candidates for embodied agents, providing both perception and reasoning abilities. Vlaser, built on Intern VL3 (Zhu et al. 2025). integrates embodied reasoning with robot control for embodied agents. While Intern VL.3 excels in multimodal
and linguistic tasks across various model sizes, Vlaser focuses on two sizes-2B and 8B optimized
for the computational constraints of robots. These models utilize VnternViT (Chen al.
2024 as
the vision encoder, paired with Qwen2.5-1.5B and Qwen2.5-7B LLMs (Qwen et al., 2025) Unlike
typical multimodal MLLMs, Vlaser emphasizes embodied common-sense reasoning and end-to-end robot control capabilities.
Action Expert There are large number of MLLMs (Team al.) 2025a NVIDIA al. 2025a)
that enhance the ability of embodied common-senue reasoning for agents, while few approaches equip the embodied MILLMs with end-to-end robot control. Vlaser extends the MILLMs with a low-level robot control and verifies the capability of different data streams in downstream VIA finetuning. Following (Intelligence et al. 2025 we design an action expert based on the open- source vision-languag, model (Chen et al. 2024 Zhu et al. 2025). Mcanwhile,
utilize the flow matching (Lipman al. 2023a) for action prediction based on the llava-like vision-language structure, while sharing the self attentonn among the language model and action expert module. Specifically, we encode the robot state as a state token and noised actions as action tokens, and input them into the utilize non-causal attention for the VLA stream. During inference, we
action expert. Meanwhile, denoise the actions based on the image observation, language instruction, as well as the current robot state.
2.2 VLASER DATA ENGINE
This section outlines the composition of the Vlaser-6M data engine, a cornerstone for the model's embodied reasoning capabilities. Here we present the overall data scale and sources for each reasoning modality, while more details about the construction methodologies are provided in Appendix [A.2
Embodied Grounding Data The Vlaser dataset incorporates two distinct 2D grounding for- mats- bounding boxes and center points- normalized to the range [O, 1000] to ensure consistent and iisolution-invariant grounding predictions across diverse image resolutions. Specifically, we collect 1.5 million high-quality question- question-answer answer pairs that support multiple grounding tasks: pre- dicting bounding boxes from ooca-vocayulary descriptions, localizing object center points based on textual descriptions, and identifying objects from given spatial coordinates. The data sourced from several open embodied grounding datasets, including RoboPoint (Yuan et al., 2024). ShareR- obot (Ji et al., (025), Pixmo- PPints (Deitke et al.) 2025). Paco-LaVIS (Ramanathan et al. (023). and RefSpatial (Zhou et al. 2025a). To further enhance generalization capabilities for open-world and open-vocabulary scenarios, we also generate an additional 300k point and bounding box annotations derived from segmentation masks in the SA-IB dataset (Kirillov et al. (002).. This combination of curated human annotations and synthetically enriched data aims to bolster both the diversity and scalability of visual grounding under real-world embodied settings.
General and Spatial Reasoning Data The Vlaser dataset integrates 1.2 million question-answer pairs dedicated general Robotic Visual Question Answering (RoboVQA) tasks, along with an additional 500k data items specifically designed to enhance spatial intelligence. This comprehensive data composition substantially strengthens the model's capabilities general state perception and 3D spatial reasoning. For the Robo VQA component, data is aggregated from multiple established sources, including RoboVQA (Sermanet et al. 2024), Robo2VLM (Chen et al. 2025b), RoboPoint (Yuan et al.) 2024). RefSpatial (Zhou et al. 2025a). OWMM-Agent (Chen al. 2025a). among others. To support spatial understanding and reasoning, we incorporate open-source datasets such as SPAR (Zhang et al. 2025), 151k (Ouyang et al. 2025). and VILASR (Wu et al. 2025). Furthermore, augment these with 100k manually annotated spatial understanding samples generated from publicly available 3D scene Latasets-includiigg ScanNet (Dai et al.; 2017), ScanNet++ (Yeshwanth et al.
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Published as a conference paper at ICLR 2026
2023), CA-1M [Lazarow al.
2025), and ARKitScenes (Baruch al. 2021 The integration of these diverse and high-quality data sources effectively enhances the model's spatial awareness and supports more robust performance in complex embodied reasoning tasks.
Planning Data To tackle complex tasks, is essential to decompose them into manageable sub-tasks and solve them step by step. This capability is commonly referred as planning. Effective planning allows robots to combine basic skills and generalize to new scenarios. We collected 400k training data to strengthen the model's planning ability, encompassing both language-based planning data and multimodal tasks. These include Alpaca-155 1aaca-15k-Inan 155k-nsstuctioon (Wu et al. 2023) and MuEP (Li et al. 2024a). To further enhance environmental understanding and reasoning for complex decision- making. we incorporated training data with detailed reasoning processes from WAP (Shi et al., (025). To improve the model's 8 ability to comprehend complex instructions and execute tasks, we followed the annotations of LLaRP (Szot et al. (024) to initialize planning tasks in Habitat (Szot et al. (2021) and
generate planning trajectories to accomplish these tasks. In addition, we integrated egocentric video
datasets such EgoPlan-IT (Chen et al. 2023 and EgoCOT (Mu al. 2023). which closely align with the observational perspective of embodied agents and provide valuable planning examples.
In-Domain Data for downstream VLAs To facilitate the end-to-end policy learning for Vision- Language Action Models (VLAs), we further generate 2 million in-domain multimodal question- answer pairs tailored for VLM pretraining. These data specifically designed to align with the embodied reasoning context and enhance the model's ability to perceive, reason, and plan in interactive environments. The in-domain data is sourced from simulation platforms SimplerEnv (Li et al. (024d) and RoboTTwi (Chen et al. 2025c). Within SimplerEnv. data is generated for two distinct robotic embodiments: the Google Robot (Brohan al. 2023b O'Neill al. 2024) and the WidowX Robot (Walke et al.) (203a)), and within Twin, data is generated from dual-arm Aloha- AgileX Robot. The question-answer answer pairs encompass the specialized categories including embodied grounding, spatial intelligence, planning and general VQA for robot states as described above. The detailed methodology for constructing and filtering each of the in-domain data in simulation described in Appendix A.2.
2.3 TRAINING RECIPE
Vlaser adopts a two-stage training recipe, designed optimize both embodied reasoning and robot control. It includes a VLM pretraining followed by a VLA finetuning. In this section, we elaborate on the training recipe among all phrases.
Dision-Language Pretraining Vlaser is developed by supervised fine-tuning (SFT) InternVL3 Zhu al.) (025) on mubboiiedrelatte datasets, including those focused on grounding, planning, and spatial intelligence. In the first training phase, we fine-tune InternVL3 using regrrreegees language modeling loss. In particular, given the input images x E R(xhxxx3 and textual prompt y T.. the language modeling loss fim can be defined by
Lim = -
where p E IRm is the next-token probability and m denotes the vocabulary size. Here, (.).) denotes the ViT and the MLP, and 0, is their parameters. FF(-) is the textual tokenizer. θ are the parameters of the LLM. & denotes the i-th predicted word.
Lision-Languaae-Actiio Finetuning For robot policy learning, we optimize the model using an additional incorporated action expert module trained on robot-specific datasets. The action expert is analogous to a mixture of experts(MoE) (Shazeer al. 2017b] Du et al. [022] Zhou et al.) 2024) architecture with two mixture elements, while the original part of parameter is used for image and text inputs, and the additionally separate set of weights for the robotics- specifcc (action and state) tokens inputs and outputs are referred as the action expert. Vlaser integrates a flow-matching matchhng--assed based action expert to predict a sequence of future actions from a single-frame observation. Specifically, we denote the action chunk A, [ar. ac+l,... ... ac+ - where a; represents the action in the current timestep t and H represents the action horizon. O, = I!...., I.,, qi] indicates the observations (image I; with views, language and robot state qı) at action timestep t. E, l and qi encoded via corresponding encoders and then projected via a linear projection layer into the same embedding space. 0 represents the action expert network and [0, 1] represents the flow matching timesteps.
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Table Comparison with existing close-sourced, open-sourced and embodied-related VLMs on 12 general embodied reasoning benchmarks, spanning from embodied QA, planning, embodied grounding to spatial intelligence and close-loop simulation evaluation. Avg denotes the normal- ized average performance of all the benchmarks, The best, second best and third best score among all baselines are colored in red, orange and yellow.
Simulatin Ege-Plnn Where2plare Pointurena Pace-Lovis Fosme-Puiata SlBlecch RefSputial MMSIBBench VLABcoch EB-ALFRED EB-Hobila
Endediaa Grounding
Spaiial latellgsnee
Ang
Clonsfarace
2P2-1-20244120
43.0
41.8
29.1
29.5
16.1
10.3
42.5
30.3
29.3
56.3
59.0
24.2
Claude 3.7 Sonnet
35.5
413
25.6
22.2
12.4
7.2
47.0
7.7
30.2
41.7
67.0
05.7
13.6
0.5.n-2..-Prr
35.0
42.9
39.9
62.8
43.5
25.8
43.4
30.3
36.9
34.8
62.7
33.0
44.4
Swalt MILAMc
7.1
1944
35.3
30.3
31.0
41.7
67.4
36.5
27.9
24.9
26.5
31.3
19.7
31.6
Lumbodied--R R1-333
36.0
16.11
15.1
65.3
68.3
36,0
23110
25.5
26.11
24.6
7.
19.3
12.5
RobeBeais20033
37.3
41.3
64.2
46.0
67.6
36,9
28.8
66.3
2688
18.1
10.0
25.3
Vlaser 28
MILAs:
Magina
5.5
Rommo-RResoon--
39.3
26:9
11.4
00.3
01.8
23.6
33.9
26.0
35.5
53
26.2
VeBriin 7B
36,3
27.3
33.1
38.9
55.1
20.1
20.9
20.6
28.3
25.9
5.7
12.3
28.8
3 SB
25,3
40.0
10.0
14.2
21.3
5,7
42.1
5.6
25.7
24.7
19.0
23.7
22.3
Uwent2
56.3
63.
35.4
Emhodies-RA B---------------
35.5
RobeBrain2.0
89.5
57.5
6.6
Vlaser NB
43.0 5334
69.5
003
68.3
40.5
00.3
59.3
272
45.0
5000
40.0
$1.3
For the action chunk, A, T (1- the corresponding noisy action chunk, and we train the network to output va(Aī. or) to match the denoising vector field u(A]|Ar) = A, where IO, Therefore, the VLA optimization loss as follows.
Leta Ep(A,)))) Il o.) o.) (A)ll
(2)
Formally, following prior flow matching based VLA works (Black et al. 2024 Zren 2025). We
sample the action chunks from the robot episodes and flow-matching timesteps optimize the network. At inference, we generate actions by integrating the learned vector field from 7 = 0 to starting with random noise A (0 I), as follows,
A A = AT + 8vo (V7(A,,00
(3)
where 8 is the integration step size. In our experiments, we H 4, and 8 as 0.1 10 integration steps) at inference time for the improvement of inference efficiency. We aim to identify the most effective VLMs for downstream VLA fine-tuning and bridge the gap between foundational VLMs and their performance in downstream VLA tasks, thus shedding light on the future construction of embodied VLMs. Currently, the SimplerEnv benchmark, including Bridge (Walke et al., 2023b) and Google Robot (Jang et al., 2022, Brohhan al.) 20236) datasets, provides numerous training episodes (Over 5M images) and corresponding Real-to-Sim benchmarks. We thus majorly analyze the most effective data stream for VLA finetuning based on SimplerEnv.
3 EXPERIMENTS
3.1 PERFORMANCE ON EMBODIED REASONING CAPABILITY
Evaluation Datasets We conduct a comprehensive evaluation of embodied reasoning capabilities across total of 12 benchmarks, covering a wide spectrum of tasks including embodied question answering, task planning, embodied grounding, spatial intelligence, and closed-loop simulation evaluation. The evaluated benchmarks consist of: ERQA (Team et al. (002b), Ego-Plan2 (Qiu et al.
2024). Where2place (Yuan et al.) 2024)) Pointarena (Cheng et 2025), Paco- Lavis (Ramanathan et al. 2023), Pixmo-Points (Deitke et al. 2025), VSI-Bench (Yang et al., 2025b). RefSpatial- Bench (Zhou et al.) 2025a). MMSch (Yang al. 2025d), VLABench (Zhang et al.) 2024). and EmbodiedBench (Yang et al. 2025c). For EmbodiedBench, we further assess performance in two simulation environments ALFRED (Shridhar al. 2020) and Habitat (Szot et al. 2021).

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Published as a conference paper at ICLR 2026
Table 2: SimplerEnv Evaluation on WidowX Robot Tasks. Avg indicates the average success rate
among the four tasks. Model sizes are indicated within parentheses. The result of RT-1-X (Brohan
al. 2023b), Octo-Base (Team et al. (024). OpenVLA (Kim et al. 2024), RoboVLM (Liu et al.
2025
and SpatialVLA (Qu al. 2025b) are from (Qu et al. 2025b) while the results of (Black
et al.
2024 from (Zren 2025
Model
Carrot on plate Put eggplant basket Spoon on towel Stack Cube
Avg
RT-1-X (35M) (Brohan et al.
2023b
4.2%
0%
0%
0%
1.1%
Octo-Base (93M) Team et al.
2024
8.3%
43.1%
12.5%
31.9%
16.0%
OpenVLLA (7B) O OpenAll 2023
0%
4.1%
0%
0%
1.0%
RohoVI.M (2R)
2025
25.0%
58.3%
29.2%
12.5%
31.3%
SpatialVLA (4B) Qu et al.
2025b
25.0%
100.0%
16.7%
62.5%
42.7%
TTo (3B) Black 2024
55.8%
79.2%
63.3%
21.3%
54.9%
InterVL3--2B
42.9%
57.1%
55.8%
11.3%
41.8%
Vlaser-OOD (2B)
60.8%
35.4%
56.7%
20.0%
43.2%
Vlaser- QA (2B)
55.8%
83.3%
77.9%
33.3%
62.6%
Vlaser-Spatial (2B)
48.3%
81.7%
76.7%
36.7%
60.8%
Vrouer-Grundinng (2B)
47.5%
80.8%
80.0%
39.6%
62.0%
Vlaser-All (2B)
52.5%
87.9%
76.6%
43.3%
65.1%
Baselines Since our method, Vlaser is trained at two model scales 2B and 8B parameters, categorize the compared baseline methods into three groups for a systematic evaluation: 1) State-of- the-art closed-source models, including GPT-4o (OpenAl 2025). Claude-3.7-Sonn
(Anthropic
2025), and Gemini-2.5-Pro (Comanici et al. 2025): ; 2) Small-scale MLLMs (2B- 3B parameters),
comprising ChatVLA-22 (Zhou et 2025b), Intern VL3-2B (Zhu et al. 2025), Qwen2.5-VL.
3B (Bai et al. (025), Embodied-RI-3B (uuan et al. 2025), and RoboBrain2.0-3B (Team et al.
2025at; 3) Medium-scale MILMs (7B 8B parameters), including Magma-8B Yang al.
2025a Cosmos-Reasonl
(NVIDIA et al., 2025a). VeBrain-7B (Luo et al. 2025).
Intern VL.3-
8B (Zhu al. 2025), Qwen2.5. VL. (Bai et al. 2025). Embodied-RUY RI-7B Yuan et 2025). and
RoboBrain2.0-77 (Team et al. (025a).
The overall experimental results are presented in Table | As shown Table compared to the base models Intern VI.3-BB and Intern used as initialization for our supervised finetuning, our Vlaser yields substantial improvements across all embodied reasoning capabilities, with particularly notable gains in embodied grounding and simulation-based evaluation. For example, the average score increases from 15.2 to 45.3 for the 2B model, and from 22.3 to 51.3 for the 8B model, These significant performance gains underscore the high quality and effectiveness of the Vlaser-6M dataset in enhancing embodied reasoning abilities. An interesting observation emerges that when finetuning on the same Vlaser-6M dataset, a smaller sized Vlaser-2B outperforms Vlaser-8B on simple point grounding tasks that require direct, short answers. Conversely, Vlaser-8B demonstrates superior performance on more complex tasks such as multi-step planning and closed-loop simulation evaluation, which often benefit from -fthougttt (CoT) reasoning. This scaling behavior indicates the importance appropriate model size selection based on target application requirements.
When compared against current of-te-of-the-art embodied-speciie Brain2.0 (Team et al. 2025a) and Embodied-Ri (Yuan al., 2025). our method, Vlaser still achieves superior performance on the majority of benchmarks while remaining highly competitive on the remainder, ultimately attaining the highest overall score (by + +10% margin overall). These results indicate that Vlaser delivers coll-balanced and robust capability set, performing strongly across multiple dimensions embodied intelligence from embodied question answering and state estima- tion future action planning. visual grounding, spatial reasoning, and closed-loop simulation. Such comprehensive competence highlights its suitability as a versatile backbone for embodied AI brains. In the following section, further examine how these enhanced reasoning capabilities, embedded within VLMs, translate into improved performance when fine-tuned for downstream Vision-Language Action models (VLAs) in simulation manipulation scenarios.
VLMs, including Robo-
3.2 PERFORMANCE ON DOWNSTREAM CLOSE-LOOP ROBOT TASKS
Finetuning Datasets We firstly conduct extensive experiments on SimplerENV (Li et al. 2024d)
to evaluate the performance of Vlaser and Vlaser data engine closed-loop robotic manipulation
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Table 3: Comparison with existing methods in SimplerEnv on Google Robot tasks. Avg indicates the average success rate among the three tasks. Model sizes are indicated within parentheses. The
results of Trace (Zheng et al. 2024), RT- 1-X (Brohan et al. 2023b). Octo-Base (Team et al..
2024), OpenVLA (Kim et al., 2024), RoboVLM (Liu et al. 2025j. Emma-X (Sun et al., 2024). Magma ( (Yang et al. alla|l]2.1. (022a)), GROOT N1.5 et al. 2025b) and To (Black et al.
2024 are
from (Lee et al. 2025).
Visual Matching
Variant Aggregation
Model
Avg
Avg
Pick Coke Can Move Near
Drawer
Pick Coke
Can Move Near Drawer
TTaceVLA (7B)
Zheng et al.
2024
28.0%
53.7%
57.0%
42.0%
60.0%
56.4%
31.0%
45.0 15.0%
RT (35M)
Bmhhan et al.
2023h
56.7%
31.7%
59.7%
53.4%
49.0%
32.3%
29.4%
39.6%
Octo-Base (93M)
Team et 2024
17.0%
4.2%
22.7%
16.8%
0.6%
3.1%
1.1%
1.1%
OpenVLA (7B)
Kum el al. 2024
16.3%
46.2%
35.6%
27.7%
54.5%
47.7%
17.7%
39.8%
RoboVLM (2B)
[Luu et al. 2025
77.3%
61.7%
43.5%
63.4%
75.6%
60.0%
10.6%
51.3%
Emma-X (7B) Sun et al.
2024
2.3%
3.3%
18.3%
8.0%
5.3%
7.3%
20.5%
11.0%
Magma (8B) Yang et al.
2025a
56.0%
65.4%
83.7%
68.4%
53.4%
65.7%
68.8%
62.6%
GRØOT N1.5 (2.11))B(VV)))
IDA et
2025b
69.3%
68.7%
35.8%
52.4%
46.7%
62.9%
17.5%
43.7%
No (3B) (Black et al. 2024
72.7%
65.3%
38.3%
58.3%
75.2%
63.7%
25.6%
54.8%
IntemVL3-22
94.3%
78.8%
19.0%
64.0%
80.4%
72.7%
11.1%
54.7%
Vlaser (00)2))))))
85.0%
76.3%
44.9%
68.7%
74.4%
69.2%
10.3%
51.3%
Vlaser-QA (2B)
90.0%%
84.2%
44.4%
72.9%
78.2%
78.2%
13.0%
56.4%
Slaser-Spatial (2B)
83.0%
77.9%
56.0%
72.3%
77.7%
73.2%
13.2%
54.7%
Vlaser Grounding (2B)
83.3%
83.3%
54,2%
73.6%
81.2%
76.8%
17.0%
58.3%
Vlaser-All (2B)
91.0%
85.4%
52.1%
76.2%
80.5%
77.7%
18.8%
59.0%
tasks. SimplerENV an open-source suite of purpose-built simulated environments with nearly 150K episodes for evaluating real-world robot manipulation policies in scalable, reproducible way. targets the key real-to-sim gaps - control and vision so that simulated performance reliably tracks real-robot outcomes. Across Google Robot and Widide//rrideeDatt V2 setups, SimplerEnv reports correlations and faithfully reflects behavior under distribution shifts, cnabling fast, comparable policy assessment without full digital twins. As a result, SimplerENV has been widely adopted for evaluating VLA models and has proven to reliably reflect the performance of the models on the real robot platform. To further demonstrate the generalizability of our method to other simulation platforms and embodiments, we also conduct experiments on RoboTwin (Mu et al. 2024 Chen et al. 2025c) platforms with Aloha-AgileX as bimanual embodiement. Robotwin is a scalable framework for bimanual manipulation, which integrates scalable training sets and pre-defined tasks as benchmarks for comprehensive robust bimanual manipulation.
strong
Table 4: Robotwin Evaluation on Aloha-AgileX Robot Tasks. Avg indicates the average success rate among the 12 tasks. The results of RDT-IB (Liu et al., 2024) are from our self-implemented training for 30k steps, which aligns the training setting with Vlaser.
Simulation Task
RDT--IB
2004
IntermVT.3-2B
Vlase--OOd VlaserQA Vlaser-Spatial Vleer-Grouningg (2B) (2B)
Reat block hammer
2856
12%
2015
18%
2016
52%
40%
Click bell
78%
94%
489
92%
Handover mic
92%
74%
55%
86%
50%
52%
84%
Move cas pot
44%
40%
42%
66%
5676
50%
46%
Move pullbortes pad
62%
70%
65%
72%
Move playiggaad away
20%
64%
52%
58%
58%
84%
74%%
Pick diverse bottles
3016
44%
3616
Place burger fiies
42%
36%
55%
88%
82%
45%
42%
Place container plate
83%
72%
70%
86%
75%
R2%
84%
Place phone
42%
40%
Place mouse pod
256
68%
30%
44%
38%
48%
92%
Shake horrle
92%
90%
Avg.
56.8%
55.858
54.5%
60.7%
61.25
60.7%
67.5%
Baselines Alongside comparisons with other commonly used VLA models (Black et al.) 2024 Kim et al., 2024 Liu et al., 2025), we conduct a clear self-comparable ablation study to evaluate the individual contributions of different in-domain data sources. Specifically, MnternVL3-22 denotes the base IntervVL model, while Vlaser-OOD refers to the Vlaser-2B model fine-tuned solely on Vlaser- 6M out-of-domain (OOD) data specific for the embodied reasoning benchmarks in Sec. [.1 without any in io-domain data in Vlaser-6M dataset. Regarding the in-domain datas derived directly from the simulation platform, we categorize them into three types: embodied QA (including planning), embod- ied spatial intelligence, and embodied grounding. The corresponding fine-tuned Vlaser-2B models
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Table 5: Ablation Studies on WidowX Robot Tasks
Model
Predict Length Execute Length Sample Steps
Carrol on plate Put egsplant in basket Spoon on the towel Stack cube
Avg
10
42.9%
57.1%
55.8%
11.3%
41.8%
22.9%
18.3%
21.2%
InternV1-22B
2
10
34. 6%
22.9%
54,2%
2.9%
28.7%
4
38.8%
54,2%
51.3%
8.3%
38.24
35.4%
56.7%
43.2%
Vlase-00DD(B3)
2
10
50,0%
21.7%
30.0%%
12.1%
28.5%
2
10
62.5%
19.2%
49.2%
21.3%
38.1%
57.5%
29.25%
54.6%
99.6%
4
10
55.8%
83.3%
77.9%
33.3%
62.6%
ViaserQAA(2B)
2
10
44.2%
64.2%
59.6%
36.3%
51.1%
2
47.5%
66.31%
67.11%
36.3%
54.3%
20
56.3%
85.0%
76.7%
35.0%6
63.3%
are denoted as Vlaser-QA LAs Vlaser-Spatial, and Vlaser-Grounding, respectively. Additionally, we combine all the three in-domain data types to fine-tune model referred to as Vlaser-All.
The full experimental results are presented in Table 2 Table 3 and Table Notably, no clear improve- ment was observed when using (laser-OO--22 The task success rates of Vlaser-OOD and the baseline InternVL3-2B remain close. Conversely, all models fine-tuned with in-domain data valta-Vaase-QAA, Vlaser-Spatial, Vlouer-Grounding. and Vlaser-All -eiiebii significant performance gains, even though the model architecture and size remain unchanged. This observation illustrates the effectiveness of our Vlaser data engine, and meanwhile identifies that there is positive correlation between common embodied reasoning benchmarks and the performance of closed-loop control of the lower level for the specific embodiment of the robot. We reckon it is the domain shift between the internet data and the corresponding robot embodiment (e.g.. WidowX or Google Robot), and we find that the enhanced abilities in the same observation domain effectively facilitate the closed-loop success rate. Therefore, it urgent shrink the domain gap between the foundational models and real- world robot embodiment for closed- task completion.
initial backbone across all three benchmarks.
Regarding the three types of in in-domain data annotations, we experimentally find that incorporating any of them leads significant performance gains over the baseline. We attribute these improvements primarily to the mitigation of visual observation domain shift. Furthermore, integrating all three types of in-domain data results in further performance enhancement. This suggests that pretraining with diverse in-domain multimodal data, spanning general QA, grounding, and spatial intelligence, could best facilitates transfer learning for VLA policy learning and leads to improved task success rates.
3.3 ABLATION STUDIES
In this section, we adopt ablation studies regarding three key hyperparameters for VLA end-to-end training, i.e., the predicted action length P. the execute action length sizes H well flow- matching sampling steps d-11 By default we use as 4, with as 4, and 5-1 as 10. The results are shown in Table 5 It is clear see that, no matter in which group of hyperparameter settings, the performance based on Vlaser-OOD shows slight improvement compared with the performance based on V Besides, there significant gains while using the model fine-tuned with in-domain data Vlaser-QA. This conclusion is as same as the results in 3.2 which demonstrates great robustness of our method.
4 RELATED WORK
Vision-Languaan Model for Embodied Reasoning Enhancing the embodied reasoning capabilities of current of-te-of-the-art Vision-Language Models (VLMs) has emerged as a critical research direction. These capabilities encompass range of competencies, including grounding (Yuan et al.) 2024, Deitke et al., 2025 Cheng et al., 2025) which identifies affordances that enable embodied agents to perform manipulations, spatial intelligence (Yang et al. 2025b d), such object counting and spatial relationship understanding, as well as task planning (Chen et al. 2023 Qiu et al. 2024), which involves assessing the current state and determining subsequent actions to be executed. Gemini Robotics-ER (Team et al. 2025b) integrates embodied reasoning into its core visual- Langua- model (VLM), in parallel, number of data-driven methodologies have emerged to support such reasoning

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capabilities. For instance, Dosmos-Reason (NVIDIA et al., 2025a). VeBrain (uoo et al. 2025).
MolmoAct (Lee et al. 2025). and EmbodiedOne Vision (Qu et al. 2025a each contribute curated datasets specifically designed for embodied reasoning tasks, emphasizing aspects such as multi-modal
instruction following and action-aware issual-languge alignment. Furthermore, several frameworks
including the RoboBrain series (Ji et al. 2025 Team al. 2025a), Embodied R1 (Yuan et al. (025).
and Robix (Fang et al. (0255) incorporate Reinforcement Fine-Tuning (RFT) and synthesize spatio- temporal reasoning datasets enriched with structured thought traces. These approaches aim to enhance models' capacity for causal reasoning and long- nog-hhrizon task decomposition. Distinguished from these prior efforts, our work not only achieves competitive, and in some cases superior performance on established embodied reasoning benchmarks, but also provides an in-depth analysis of the synergistic relationship between pre-trained VLMs and downstream Vision-Language Action Models (VLAs), offering insights that bridge model capabilities and real-world deployment.
Vision-Language-AAA ction models. Developing a generalist model remains a central challenge in robotics. Inspired by the strong generalization abilities of vision-language models (VLMs) (OpenAI
Action
2023 Chen et al.] 2024| Bai et al. 2025; Team et al. 2023) trained on large-scale internet data,
researchers have proposed vision-lanen language action (VLA) models, which have demonstrated promising
performance (Brohan et al. 2023a Kim et al.) 2024 Qu et al.] 20225b] Hou et al. (025). Compared
to traditional robot policies, VLA models pretrained on large-scale robotics datasets and exhibit
improved generalization across object categories and visual observations. Building on recent progress, researchers have incorporated techniques such as diffusion (Ho et al.) 2020 Rombach al., [022] Peebles & Xie 2023), flow matching (Lipman et al. (2023b), and mixture-of-experts (MoE) (Shazeer 2017a) into VLA models, and have adopted larger, more capable VLMs as their backbones. These advances have enabled VLA models to tackle wider range of complex real- world manipulation tasks. Efforts have been made to enhance specific reasoning abilities in embodied scenarios (Team et al. 2025b Ji et al. 2025 NVIDIA et al. 2025a). In parallel, several studies (Intelligence et al. Driess et al., 2025: hhou et al., 2025b) explore unified training frameworks for VLMs and VLAs leverage the reasoning capacity of VLMs. However, the relationship between high-level multimodal reasoning and low-level control performance remains largely unexplored. It is still unclear which specific multimodal abilities such spatial understanding. grounding, or planning and which types of training data most effectively enhance the control capabilities of a VLA model. In this work, take initial step toward analyzing this relationship by a systematic evaluation, and also propose the latest foundational model with strong embodied multimodal understanding and action prediction.
2025
5 LIMITATIONS
One limitation of this work is the absence of real-robot experiments. While the experiments conducted in this study have all been performed well in simulation, in future work we plan to conduct more experiments with real robots further evaluate and refine the proposed methods.
6 CONCLUSION AND DISCUSSION
We introduce Vlaser, foundational Aision-language-acti model that extends vision-language models with embodied reasoning and end-to-end robot control capabilities. Powered by the Vlaser- 6M dataset, the model establishes new state of the art across a wide range of embodied reasoning benchmarks, including planning, grounding, spatial reasoning, and simulation- based tasks. Moreover, Vlaser reveals the most effective data streams for downstream VLA through its curated data pipeline, achieving state-of-the-art performance on Bridge and competitive results on Google Robot for end-to-end robot control.
In this work, we reveal that current embodied reasoning benchmarks exhibit significant domain gap when compared to real-world robots. This core domain shift arises from the observation that robots have a fundamentally different viewpoint from that of internet datasets. Additionally, there are inherent limitations due to the lack of sufficient data from the robot's 's perspective, despite the abundance of vision datasets available. Therefore, argue that it is essential to develop alignment techniques to bridge the domain gap in representations between the robot's 's viewpoint and that of internet datasets.
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ACKNOWLEDGMENTS
This work is supported by Shanghai Artificial Intelligence Laboratory.
ETHICS STATEMENT
This research adheres to the ICLR 2026 ethical guidelines and upholds the principles of responsible research. We ensure that no personally identifiable, sensitive, or harmful data were used. Our experiments were based publicly available datasets and did not involve any human subjects or vulnerable groups. We have considered the potential societal impact of our methods, including the risk of misuse, and believe that these contributions primarily advance scientific understanding and do not pose foreseeabl harm.
REPRODUCIBILITY STATEMENT
We follow the reproducibility guidelines in the ICLR 2026 author guidelines. We will open source code, configuration files, and scripts to reproduce our results, including dataset construction, model training, and evaluation, on platforms such as GitHub and Huggingface soon as possible.
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A APPENDIX
THE USE OF LARGE LANGUAGE MODELS (LLMS)
We used large language model, ChatGPT (OpenAl 2025), only for grammar check and correction. All the writings was manually reviewed by the authors. Crucially, Al was not used to generate any research data, statistical analyses, figures, or conclusions. The authors take full responsibility for the final content and any errors herein.
A.1 TRAINING DETAILS
Our Vlaser is optimized in a fully supervised finetuning (SFT) manner based on Intern VL3 series Zhu al. [I2 VL3-2B and IntemnVL3-8B), To maximize adaptation embodied reasoning tasks, we keep all parameters trainable, including those in the large language model, the vision-language projector, and the visual encoder, enabling comprehensive end-to-end learning. Further details regarding the training setup, including hyperparameters and optimization settings, are provided in Table 6
Table 6: Hyper-parameters used in the VLM pretraining of Vlaser.
Configurations
Values
LLM sequence length
16, 384
Dynamic Resolution
True
Patch Size
448
Max Patch num
12
Freeze vision tower
False
Freeze multimodal projector
False
Freeze language model
False
Optimizer
Adam W
Optimizer hyperparameters
81 = 0.9, /3 0.999, eps le
Peak learning rate
2c-5
Learning rate schedule
cosine decay
Training epochs
1
Training steps
5, 000
Warm-up steps
150
Global batch size
128
Gradient accumulation
2
Numerical precision
6
While using Vlaser as the base model for downstream VLA Policy fine-tuning, optimize all parameters within both the VLM and the Action Expert. Additionally, we conduct comparative experiments using several different versions of base models, including Intern etc. Detailed information and related parameter settings can be found Table 7
A.2 DATA GENERATION DETAILS
Embodied Grounding Data To further enhance embodied grounding capabilities, we generate an additional 300k high-quality data samples from the SA-1B dataset (Kirillov al. 2023). The
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Table 7:
used in the VLA fine-tuning.
Configurations
Values
LLM sequence length
384
Image Size
448
Freeze VLM
Falsc
Global batch size
1024
Training epochs
VLM Peak Learning Rate
5e-5
Action Expert Peak Learning Rate
Se-5
Optimizer
AdamW
Optimizer hyperparameters
81 0.9, 82
0.999, eps 1e
Observation history length
Action Chunk length
4
Execute Action length
2
data generation process consists of two main stages. First, we convert segmentation masks into bounding boxes and point annotations: bounding boxes are derived by computing the minimal axis- aligned rectangle enclosing each mask, while point annotations are obtained by randomly sampling coordinate within the mask region. To ensure annotation quality, we apply an loU threshold of 0.9 to select high-precision masks; masks with lower IoU values are either excluded or assigned reduced sampling weight. From the over 1 billion available masks, we initially sample I million candidate instances. In the second stage, we employ two-step captioning and refinement pipeline. Coarse captions are first generated using BLIP-2 (Li et al. 2023), followed by a filtering and refinement process using Qwen2.5- -wen2.5-VL-7B (Bai et al. 2025) to eliminate low-quality items and produce more accurate and detailed descriptions. This rigorous pipeline ultimately yields 300k high-quality data samples tailored for embodied grounding tasks, significantly expanding the diversity and precision of our training corpus.
Spatial Reasoning Data To enhance spatial intelligence capabilities, we manually construct dataset of 100k 3D spatial perception samples derived from ScanNet (Dai et al. (017)). ScanNet++ (Yesh- wanth al. 2023), and ARKitScenes (Baruch al.) (02T). Following methodologies established in prior data engines (Deng et al., [025 Fan et al. 2025). we utilize both the 3D point cloud and video sequences of each scene to construct spatio-temporal scene graph. This graph encapsulates structural and semantic information such as overall scene dimensions, room center coordinates, object category counts, and precise 3D bounding boxes for every object instance. Based on this representation, we generate spatial reasoning questions that probe layout properties and inter-object relationships. These include queries about the object counts, absolute and relative distances, object and room sizes, relative directions between objects, and other spatial attributes, using the same question template in VSI-Bench (Yang et al. 2025b).
Planning Data To improve the model's ability to comprehend complex instructions and execute tasks with environmental feedback, we curate additional planning data within the Habitat simulator (Szot
al. 2021). Specifically, we initialize each planning task following the annotations of LLaRP (Zot
et al. 2024), which specify both the task goals and the set of permissible actions. An L.M agent based on gpt-4o (OpenAl (025) is then deployed roll the task. During each rollout, we record the task
instruction, the sequence of actions taken, and the environment's 's feedback, including observations and success signal for each action. Both the executed action trajectories and the corresponding feedback are retained. Only trajectories that successfully accomplish the task are included in the final training set, providing rich paired data of instructions, execution processes, and environment responses for enhancing the model's planning capabilities in complex environment.
In-Domain Data for downstream VLAS We generate million in-domain multimodal data samples to facilitate direct transfer learning for downstream Vision-Language Agents (VLAs) during fine- tuning. These data are collected from two distinct simulation platforms: the Widow Robot Walke
al. 2023a) and the Google Robot (Brohan et al. 2023b a) within the SimplerEnv (Li et al.) 2024d), as well as the Alile-AgileX Robot from Robo Twin2.0 (Chen et al. (025c). The dataset mainly encompasses three systematically designed question answer types: 1) General QA, which queries the robot's ' current state and requests next few action plans; 2) Grounding QA, which requires the
robot to localize points and bounding boxes the actionable affordances; 3) Spatial Reasoning
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QA. which probes relative spatial relationships and geometric properties of objects in the scene. Detailed prompt templates and representative examples for each data category are provided in F (General QA), Figure GGrounding QA), and Figure 5 (Spatial Reasoning QA), respectively. We use 2025) as the base model to generate the above data items. Qwen2.5VL-7B (Bai et al.
To further enhance the quality and diversity of the generated in-domain QA data, implement post-processing data filtering pipeline. In line with established practices in dataset construction (Wang
al. 2023] Cui et al. [2023] ei al. 2024b), we employ an LLM-as-a-judge approach (Li et al. 2025)
to score each generated data sample on a scale of to 3, using Qwen2.5VL-322 (Bai et al. (2225) as
the judge model. The detailed instruction prompts for General QA, Spatial QA, and Grounding QA
are provided below. We filter out all samples assigned a score of 1, which account for approximately 10% of the initially generated data. This outcome reflects the overall high quality and diversity of our in-domain dataset. To further validate the reliability of the L.LM-based evaluation, we randomly selected subset of the scored data (covering all score levels from to 3) for human reassessment by three human experts. The results show that the LLM judge's scores align with human ratings in nearly 80% of cases, confirming the consistency and reliability of our automated evaluation and data filtering pipeline.
Prompt of Data filtering for General QA data
You are an expert in evaluating question-answer pairs for robol arm camera images and lask instructions. Please evaluate the quality of the generated QA pair based on Relevance, Informativeness and Clarity.
receive following inputs: The rohot arm camern image, task instruction, the generated question, generated
answer. Carefully evaluate whether the question and answer are relevant to the image and task, informative, and clearly expressed.
Scoring Criteria (1-3):
1 Poor The QA pair is irrelevant, uninformative, or unclear. The question or answer doesa't It relate to the image or task instruction. The unswer is vague, generic, or provides no useful information. The question poorly formulated or doesn't make
"How cook?" Answer: "Use a pun." " (irrleevant to zubot tusk) Question: "What color?' Answer: "Color." (unclear and uninformative)
"Exxmples:**.. xx Question: "What this?" Answer: "It's image." (too generic) Question:
Faii The QA pair is somewhat relevant but lacks depth or clarity. The question relates to the image/task but is too simple or generic. The answer provides basic information but lacks detail or specificity. Some aspects are unclear or could be hetter explained.
**Fxamples:** Question: "What objects the image?' Answer: "There
objects." " (tno vague) Question: "What is the robot doing?" Answer: "The robot is moving"" (lacks detail) Question:" "How to complete the task?" Answer: "Follow instructions." (not specific enough)
3 Good The QA puir highly relevant, informative, and clearly expressed. The question specific, well formulated, and directly relates to the image and tusk. The answer proides detailed, accurate, and useful information. Both question and answer clear and help understand the robat's environment and task.
"What objects are visible in the robot arm's workspace and which one should be manipulated based on the task instruction!" Answer: "I can
"Examples:**. - Question:
red cup. blue bowl, and green plate on the table. According to
the task instruction to pick up the cup'. the rohol should locus on the red cup located in the center of the workspuce."
Question: "What obstacles might prevent the mbot from completing the task?" Answer: "The workspace appears clear, but the larget object positioned near the edge of the table, which may require careful positioning to avoid knocking it over during manipulation
Output Requirement: You must return only single integer score from 3. Do not include any explanation, labels, or extra content. Question: (question)
Answer:
Prompt of Data filtering for Spatial QA data
You expert evaluating spatial intelligence question-answer pairs for robot camera images and task instructions. Please evaluate the quality based on Spatial Rensoning Accuracy, Detail Level, and Relevance.
You will receive the following inputs: - The rubat arm camera image, the task instruction, the generated sputial question, and the generated spatial answer.
Carefully evaluate whether the question targets spatial reasoning, and whether the answer provides accurate and detailed sparial information.
Scoring Criteria (1-3):
1- Poor The spatial QA pair lacks sputial reasoning focus or provides incoreet/vaguee spatial information. The question doesn't target spatial aspects (counting. relationships. distances, orientation, etc.). The answer provides incorrett spatial information or is too vague. The spatial reasoning is flawed or irrelevant to the task.
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**Examples:** Question: "What is in the image?" Answer: "Objects." " (not spatial) Question: "How many objects?' Answer: "Some." (vague, no specific count) Question: "Where object Answer: there." (no spatial detail)
Question: "What is the distance?" Answer: "Close." (not quantitarive)
2- -aiir The spatial QA pair addresses spatial reasoning but lacks precision or detail. - The question targets spatial could more specilic. The answer provides basic spatial information but lacks quantitative details precision. Some sparial relationships are mentioned but not fully explained
apaiaal aspects
*Fxamples: Question: "How many cups there?' Answer: "There few cups." (not specific count) Ques- tion: 'Where is the red object relative to the bluc onc?' Answer: "The red one is near the blue one." (locks specific direction/distance)) Question: "What spatial arannemmnnt" Answer: 'Objacts arranged on the table." (too general)
3 Good The spatial QA pair demonstrates strong spatial rensoning with procisc, detailed information. The question clearly targets specilic spalial aspects (counting, relatienships, distances, orientation. geometry, etc.). - The answer provides to the robot task and helps
accuralee, quamitative, and detailed spatial information. The spatial reasoning understand the workspace laynut.
e "Examplec*** Question:
"How many objects visible the scene and what are their types?" Answer: can count objocts: 2 red cups positioned on the left side of the table, blue bowl in the conter, and 2 green plates arranged on the right side." Question:
robot arm's current position"" Answer: "The larget object (red cup) is located uppruximately 30cm to the right and 15cm forward from the rubot arm's current end-effector position, requiring diagonal reach motion." Question: "Which objects are within
'What
relative position the target object compared
rohot's reachable workspace"" Answer: "Based on the robot amm's 's reach, the blue bowl (center, 25em away) and the left red cup (20cm away) are within reach. The rightmost green plate (45cm away) is outside the immeeditee reachable zone."
Output Requirement: You must retur only a single integer score from to 3. Do not include any explanation, labels, or extra content. Question: (question)
Answer: [answer]
Prompt of Data filtering for Grounding QA data
You an expert in evaluating visual grounding question-answer pairs for robot camern images and task instructions. Please evaluate the quality based on Grounding Accuracy Coordinate Validity, and Localization Precision.
You will receive the following inputs: The robot arm camera image, the task instruction, the generated grounding question, and the generated grounding answer (which should contain coordinates bounding boxes). Carefully evaluate whether the question targets object localization, and whether the provides valid and accurate coordinate information.
Scoring Criteria (I-3):
Poor grounding QA pair lacks localization focus or provides invalid/incorret coordinate information.
question doesn't 't target object localization, pointing, detection. - The answer lacks coordinate information or contains invalid coordinates (out of bounds, wrong format). - The coordinates don't 't match the described object location.
** "Examples:** Question: 'What is in the image?" Auswer: "A cup." (no coordinates)- Question: "Where is the objeet?' cup." Answer: 'The cup at (1500, 2000). (coordinates out of bounds for normalized 0. 1000 range) Question: "Find the object.' Answer: "Box: [100, 200, 50, 300)." (invalid box format, x2 <xl)
Answer: "IT's on the table." (no coordinates). Question: "Puint
The grounding QA pair addresses localization but coordinates imprecise or partially valid. The question targets localization but could be more specific. - The answer contains coordinates but they may be approximats. slightly off, or lack precision. Some coordinate information present but formatting could be improved.
Fair
*eFxamples:** Question: "Where is the object?' Answer: "The object is at position (500, 300)." (single point, but should specily muliiple objects exist) Question: "Point to cups." Answer: "Cups are at (200, 150) and (600, 400)." (coordinates present but may not he precise) Question: "Mark the houndaries." " Answer: "Box: [100, 100, 400, 300)." (valid box but may not accurately bound object)
3 Good The grounding QA pair demonstrates
precise localization with valid accurate coordinate information.
question clearly largets object localization, pointigg detection, or precise positioning. The answer provides valid, accurate coordinates (points or bounding hoxes) in the correct format. Coordinates are normalized to 1000 range and accurntely represent object locations. Multiple objects properly localized if applicable.
* "Examples:** +* Question: "Where is the red cup located in the imago? Provide coordinates." " Answer: 'The red cup is located at point (450, 320). <poine>|1450. 3/0oit/pennt>" Question: "Point to all instances of cups visible in the scene." Answer: can locate cups: red cup (450, and
cup (680, 250). <puint>|[450. 320), /680, 250]]]//oint>* Question: "Can you locate and mark the boundaries of the target object?" Answer: "The target object (red cup) bounded by box [380, 280, 520, 360). <box>||380, 280, $20, 3/0])///ux> Question: "Find and mark all instances of plates the image." Answer: 'I found plates: plate at hox [100, 200, 250, 350), plate at box [600, 180, 750, 330J. <hox> 100, 200, 250, 350], [600, 180, 750, 330]]</ox>"
Output Requirement: You must return only single integer score from to 3. Do not include any explanation, labels, or extra content. Question: (question)
Answer: (snswer)
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A.3 SIMULATION EVALUATION DETAILS
We fine-tune and evaluate the VLA models using various base models within the SimplerEny simulation environment. To ensure fair evaluation, iterations for the WidowX Robot Task and the Google Robot Task, respectively. Specifically, for the WidowX Robot Tasks, we use checkpoint after 45,390 iterations, while for the Google Robot Tasks, we use a checkpoint after 36,970 iterations. During evaluation, we utilize a single image and select an action chunk of size 2 for execution. In the flow matching configuration, we use inference steps during the inference phase and apply Euler method numerical integration method. We evaluate sufficient number of samples to ensure the reliability of the tests. The exact number test samples for each task is shown in the Table g
checkpoints with the same number of
Table 8: The number of samples evaluated on SimplerEnv.
Task Name
Evaluation Samples
Carrot on plate
240
Put eggplant in basket
240
Widowx Robot Tasks
spoon on towel
240
stack cube
240
Pick coke can
300
Visual Matching
Move Near
240
Open/Close Drawer
216
Google Robot Tasks
Pick coke
825
Variance Aggregation
Move Ncar
600
Open/Close Drawer
378
A.4 QUALITATIVE DEMONSTRATION
Qualitative Samples for Embodied Reasoning. To gain in-depth insights into Vlaser, we provide extensive visualizations on embodied reasoning tasks including Embodied QA 9 grounding [T] spatial intelligence 11 and planning tasks in Simulation 12] These examples not only validate the generalizability of Vlaser, but also confirm the superior capabilities of Vlaser across all tasks.
Qualitative Samples in SimplerEnv. Figure 0 and Figure present qualitative examples from showcases the evaluations conducted in the SimplerEnv. Specifically, showcasss the performance of multiple models on the WidowX Robot Task, while Figure demoonttaaes the results on the Google Robot Task. From these results, it is evident that, under the same test conditions, baseline models often fail the entire task due to slight positional errors. In contrast, model shows significant improvements in this regard. Whether incorporating in-domain question-answer pair data, spatial intelligence data, grounding data, the performance of the model shows clear enhancement. These results validate the effectiveness of our approach.
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analycing
images
Given the robot arm camera image and the task instruction: "[text]"
question ended and could about:
about image
question should be
General QA
Objects visible in the image The robot arm's current state or pasition
How accomplish given
challenges might
Safety considerations for the task Or other relevant aspect of the image and task
respord
Question: (Your question here) Answer: (Your detailed answer here)
Make the question natural and the informative and helpful for understanding robot arm's environment
Question: can amm effectively Instructed?
green
right
Answer: fold grean rleth from loft right, the robot should first itself so that the left edge of the cloth is aligned with the starting point of the fold. The applying gentle
should then edge. important ensure during the folding process. The robot arm should also be equipped with sensors to dothhs
in controlled
fold the cloth damaged
over
accordingly maintain and accurate fold. Additionally, the rabot arm should be programes prgrammee to stop folding the right edge is reached, ensuring that the cloth is neatly folded without any creases.
adjust its
WidowX Robot
What safety measures should be taken when using robot arm to pick up cans like the shown the image?
Question:
Answer: When using robot arm to pick up cans, several safety measures should be considered ensure the task performed safely and efficiently. First, it's crudal to ensure that the robot prevents
gripper securely accidental drops damage the can. Additionally, the robot arm should be programmed with movements
properly
any impact It's also importent to ensure that the robet arm operating within designated safety boundaries
gentle
itself.
unintended movements that could harm. Furthermore, obstacles could interfere
robot movement, and the area should be well well-IIt to ensure the robot am accurately Identify and grasp the supervisor safety system
environment
Lastly, it's robot arm's actions and intervene necessary.
Google Robot
state the rebot and how accomplish the task of lifting the pot?
Question:
What
Answer: The robot am currently pesitioned above the pot, with its grippers engaged around
pot's
To accomplish
lifting pot,
robot arm should ensure secure grip on the handle and then apply upward force lift the pot off the surface. The
should
mindful of
potential obstacles or uneven surfaces could interfere with lifting process, and maintain steady and controlled motion to avoid spilling the Safety considerations properly calibrated and that the pot within the weight capacity of the grippers. nearby objects
ensuring
Additionally, the rabot should that could be at risk being hit injured during the lifting process. Overall, the robot arm should prioritae precision, control, and safety successfully lifting pot.
Robo Twin
complete
Figure 3: An illustration of Vlaser-6M data engine for in-domain general QA sample in SimplerEnv and Twin.
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Grounding QA
Instruction: You are an All assistant specializing visual grounding analysis
robot
images and task instructions.
Given robot arm camera image and task instruction: "[text)"
WidowX Robot
Please generate natural question-anse answer pair specifically focused
grounding capabilitiss question and could ask about:
target objeect
Object pointing: "Where is the (speafic object] located the image?"
Multiple object pointing: "Foint to all the [objects] visible the scene," Object detection: "Can locate and mark the boundaries of the
[object]?"
Multiple object detection: 'Find and mark instances of [objects] the
Question: Where is pile small blue objects located in the image? Your answer should be formatted ("<hoso I, y?], [K3, y3, x4, ...I//boo""" bounding are normalized to integers between 0 and 1000. the the hounding format directly.
image."
-Spatal localization: "Where exactly cen we find the (object] that the robot needs to interact with?"
Region identifficatio: "Which area image object]"
[target
Precise positioning: "What
coordinates [object]
Image?"
Answer: <hoxe][?61, 364, 912, 608])]s/boo>
points
bounding box(es]
the answer for the speciic visual grounding question.
respond
Question: (Your grounding question here)
Answer: (Your detailed localisation with specific coordinate
information]
following:
Google Robot
coordinate_ patterns
The box format could be like the following:
patterns ||
Question: Where is the apple located in the image? should be formatted as ('spoinb:i[xl, yil. |x2, y2) ->//point>(" The point coordinates are normalized to integers between and 1930. Answer: <point>[[612, 223]]//pointz>
Make sure the question focuses on object localization, positioning, boundary detection, and the answer provides precise location information objects the robot's
directly.
Robn
identify the smooth plastic and determine precise location Your answer should be formatted as \'<paint>[[xd, ///p///ttnt// point integers between and 1900.
Question: visual
HOLE
normaliced
Answer: <point>[[565, 775), 1565, 775]|=/pointt""!
Figure 4: An illustration Vlaser-6M data engine for in-domain embodied grounding QA sample in SimplerEnv and Rwbn.win.
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Spatial Reasoning QA
Instruction:
You are assistant speciallzing in lnteligence analysis
arm images and task Instructions.
WidowX Robot
Given the robot arm camera image and the task instruction: "[text]"
Please generate natural questioonansser pair specifically focused spatial intelligence aspects. The question should target spotial reasoning and could ask about:
Object counting:
many [objects]
Question: What the reletive position of the drawer compared to the robot arm?
the scene?"
relationships: "What the relative positiin of labject A] compared to lobject a|?"
Answer: drawer is positioned directly beneath arm, with the arm's gripper currently interacting with the surface the
Distance estimation: "How far apart [object and [object DJ?"
The drawer orlented horizontalyy, its front edge aligned with the front edge of the robot arm' The drawer appears slightly tilted forward, arm process oponing
-pattil orientation: "In which direction [object]
suggesting that
Deeth perception: "Which object appears
floser/farther from camera?"
Geometric progerties ties: 'What the approximate shape/Abe of [object] the scone?"
Spatial arrangemet:
objects
arranged the workspace?"
Accessibilit analysts: 'Which objects within robot's reach based on their positions?
Collision evoidance: "What spatial constraints
Google Robot
moving toward (target
object]?"
3D spatial understanding: "Whet
vertical/hoc horizontal relationship between objects?
respond following
Question: Yourr spotial intelligence question here)
detalled analysis here]
relative position the apple on the table?
Make the
reasoning,
positioned
apple
object relationships, pesitioning, or peometric iiderstanding, and the answer provides prease spatial information about environment.
the table. can closer the top left corner the table, while the apole slightly below and the right of the
Robo Twin
AGILE
Question:
the Rubik's cube currently held by the robot arm positioned above the display reclangular
placed onto the stand without obstruction?
Answer: Yes, the Rubik's cube held above the displey stand, and sufficient
dlearance
placed anto the stand without obstruction.
aligned
with the rectangular hollow base of the stand, and the robot amm has enough space without any visible amm' 's pasition and the cube's orientation suggest that the placement can he executed smoothly precisely.
immediate vicinity.
Figure 5: An illustration of Vlaser-6M data engine for in-domain spatial reasoning QA sample in SimplerEny and Rwbo.win.
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Table 9: Embodied QA examples.
Embodied QA: Example #1 from ERQA.
m
Question:
What happened between these two frames? Choices: A. Robot arm lifted the cup. B. Robot arm poured all the nuts into cup. C. Robot arm poured some of the nuts into a cup. D. Nothing happened. Please answer directly with only the letter of the correct option and nothing else.
Vlaser-8B:
C.
Embodied QA: Example #2 from ERQA.
Question:
Select the best answer the following multiple-choice question based on the video. Respond with only the letter (A, B, C, or D) of the correct option. Considering the progress shown the video and my current observation in the last frame, what action should I take next in order to weigh dough? A. put dough on table B. pick dough C. roll dough on table D. cut dough with dough cutter
Vlaser-8B:
A.
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Table 10: Embodied Grounding Examples.
Embodied Grounding: Example #1 from PointArena.
Question:
Point to the tool people for writing.
Vlaser-8B:
<point>[[701, 374]]</point>.
Embodied Grounding: Example #2 from Where2Place
MIM!
Question:
Please point the free space between the black water bottle and the pot lid.
Vlaser-8B:
<point><1293, 560]]</point>.
Intonn//33322 283
Fall
Viosr-DA
Succoss
Carrol on the plate
egulann besset
Intern/133-20
Fall
Vlser QA
Succes
on the
Stack Cube
Figure 6: Qualitative samples in SimplerEnv on WidowX Robot Tasks
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Table 11: Spatial Intelligence Examples.
Spatial Reasoning: Example #1 from VSI.
Question:
You a robot beginning at the standing by the window and facing the window. You want to navigate to the white shoes. You will perform the following actions (Note: for each [please back,' 'turn left,' or 'turn right.'): 1. [please fill in] 2. Go forward passing the bed 3. [please fill in] 4. Go forward until the white shoes. You have reached the final destination. A. Turn Right, Turn Left B. Turn Left, Turn Left C. Turn Left, Turn Right D. Turn Right, Turn Right. Answer with the option's letter from the given choices directly.
in], choose either 'turn
Vlaser-8B:
B.
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Table 12: Simulation planning Examples.
Simulation Planning: Example #1 from EmbodiedBench.
Question:
You robot operating in home. Given task, you must accomplish the task using a defined set of actions to achieve the desired outcome. Now the human instruction is: Put microwaved tomato slice the counter. You need to output the reasoning process and an action to execute.
Vlaser-8B:
Reasoning-ste step-:: To put a microwaved tomato slice on the counter, need start by slicing a tomato. To do this, I require knife. In the image, there is a knife visible on the counter near the toaster. Finding the knife is the logical next step because it is essential for cutting the tomato into slices. Once have the knife, can proceed with slicing the tomato. Therefore, my next action is to find the knife. Action: find knife.
Reasoning-step-2: have identified the knife on the counter near the sink. Picking up the knife the next logical step because it is necessary for slicing the tomato. Once have the knife, can proceed with cutting the tomato into slices. This aligns with the goal of preparing tomato slice for microwaving and then placing it the counter. Action: pick up the knife.
Reasoning-step-1 have successfully located the knife in the drawer. Now, I need to find tomato to slice. The image shows a kitchen counter with various items, but tomato is immediately visible. Tomatoes are commonly stored the refrigerator or on the counter. Since the refrigerator is partially visible the left side of the image, it makes sense to check there first. If the tomato isnï in the fridge, can then look around the counter more thoroughly. Therefore, my next step find tomato. Action: find tomato.
Reasoning-step--; The current visual state shows the microwave with door closed, and the tomato slice is on the counter. The task is place a microwaved tomato slice on the counter, which has already been accomplished. Given that the tomato slice is already on the counter, the next logical step is confirm its placement by finding suitable counter top. This ensures that the tomato slice securely and appropriately positioned for the task. Therefore, the next step is to find counter top. Action: find a counter top.
Reasoning-step-Ble 10: The current visual state shows microwave with its door closed, and two tomatoes are visible on the counter. One tomato is already sliced, indicating that the previous steps involving slicing and microwaving have been completed. Since the tomato is currently being held, the next logical step to put it down on the counter. This action will free up the hand to handle the tomato slice and place it on the counter as required by the task. Therefore, the appropriate next action to put down the tomato. Action: put down the tomato.
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Visual Matching
Variant V Aggregation
InternVV.3228
Viaser
Success
VlaserQA
Sucress
Spatial
Success
Groandnrunniinng
Success
Pickup
coke
IntemVL332BB
Fail
Viaser
Success
Vlase-QA
Success
Spater-
Success
Groaningg
Success
Move
Move near
IntennVL.3-2B
Fail
Viaser
Vlaser-QA
Success
Vlata-Spatial
Success
Groaninn
Success
OpevClose Drawe
OpenyClse Drawer
Figure 7: Qualitative samples in SimplerEnv on Google Robot Tasks
30


Reviews:
[{"review_id": "Tq8I9VDhVw", "reviewer": "Reviewer_QnV2", "summary": "This paper presents Vlaser, a vision-language-action framework that couples a VLM for embodied reasoning with a flow-matching action expert for low-level control generation, supported by the large-scale Vlaser-6M data engine. The approach unifies grounding, spatial reasoning, and planning with closed-loop execution. Evaluations across 12 embodied benchmarks and SimplerEnv on WidowX and Google Robot demonstrate state-of-the-art performance and reveal that in-domain robot-view data drives the largest gains in downstream control tasks.", "questions": "1. In Table 3, why does Vlaser exhibit lower performance than the InternVL base model? Could the authors include an ablation study that directly fine-tunes the base model on different in-domain datasets, i.e., to produce Vlaser-QA?\n\n2. The evaluation covers only a small set of manipulation tasks on WidowX and Google Robot, making it difficult to claim generality to other embodiments or tasks. I recommend adding the second-part experiments and reporting the corresponding 8B results.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-written, with clear figures and detailed explanations that make the work easy to follow.\n\n2. The method introduces a clean end-to-end VLA design that decouples vision-language reasoning from an action expert and leverages flow matching for low-level control, enabling seamless closed-loop execution.\n\n3. The paper demonstrates state-of-the-art performance across 12 embodied-reasoning benchmarks and consistently surpasses strong baselines.\n\n4. It also conducts a systematic analysis of pretraining data effectiveness for VLA transfer and offers scaling insights.", "weaknesses": "1. The paper compares only two model scales within a single architecture and concludes that smaller models are better at point-based grounding while larger models excel at multi-step planning and closed-loop simulation; however, this scale–performance relationship may not generalize to other model families, and the work provides little deeper analysis or mechanistic explanation to substantiate it.\n\n2. The policy relies on single-frame observations and fixed-length action sequences, with the action expert employing a fixed 10-step flow-matching sampler. This design limits the evaluation of temporal reasoning and multi-view robustness. The paper does not conduct ablations on these hyperparameters. The training uses an observation history of only one frame, which may constrain long-horizon planning capabilities.", "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": "pT71V7KPd9", "reviewer": "Reviewer_cVAr", "summary": "This paper introduces Vlaser, a vision-language-action model that unifies embodied reasoning and robot control through a large-scale dataset and a dual-stage training pipeline. Built on InternVL3 and Qwen2.5 backbones with a flow-matching action expert, Vlaser achieves state-of-the-art results on 12 embodied reasoning benchmarks and strong performance on real-robot tasks like WidowX and Google Robot. The authors also provide a systematic analysis of data transfer, showing that in-domain embodied data is far more effective for policy learning than generic web data, highlighting a persistent domain gap between reasoning and control.", "questions": "1. The paper mentions “low-level control.” Does this refer to directly controlling the robot’s motors? This terminology seems to be commonly used in the robotics domain.\n2. Although the paper describes the training and inference procedures, it is still unclear what exactly the pretrained VLM passes to the VLA.\n3. In Figure 1, why are the closed-source models not included in the ranking?\n4. Explain what is the open-loop inference and closed-loop control?\n5. After adding the reasoning capability, does it affect the runtime efficiency of the VLA? Is this reasoning capability truly cost-effective?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper presents a unified framework for embodied tasks and introduces the Vlaser-6M dataset.\n2. The dataset is large, diverse, and carefully curated, covering grounding, spatial reasoning, planning, and simulation tasks.\n3. The study offers useful observations on the gap between vision-language reasoning and low-level control, providing practical guidance for future model design.", "weaknesses": "1. The detailed architecture of the action expert is not presented. In Figure 2, the structure of the action expert is not shown.\n2. I think the authors could provide more explanations or figures to help readers understand the structure of the action expert and the training process.\n3. Since the paper emphasizes the impact of VLM reasoning on VLA performance, I think the authors could add some experiments on real robots or additional simulators to demonstrate the model’s generalization capability.", "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": "rKDsNJzGL6", "reviewer": "Reviewer_ZtrG", "summary": "This paper introduces an embodied vision-language-action model built on InternVL together with the Vlaser-6M data engine. It uses a two-stage recipe, embodied VLM pretraining followed by VLA fine-tuning with a flow-matching action expert for low-level control. The paper provides an analysis of which pretraining data streams transfer best to VLA policy learning, giving practical guidance on building task-aware data mixtures. Vlaser attains state-of-the-art results across embodied reasoning benchmarks and shows competitive closed-loop manipulation performance.", "questions": "Please refer to the weakness part.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper is clearly written and easy to follow.\n* This paper tackles an important problem: bridging VLMs and VLAs via embodied-data pretraining and in-domain fine-tuning.\n* Results on embodied reasoning benchmarks and SimplerEnv are promising.", "weaknesses": "* For a more informative study, I suggest the authors to include ablations on the pretaining dataset, quantifying how each source contributes to embodied reasoning and robot control.\n\n* In the planning data curation, the authors collected data from Habitat. Did you avoid overlap with the EB-Habitat evaluation set? Additionally, please elaborate more on why Habitat was chosen for planning data over alternatives such as ALFRED.\n\n* For in-domain data curation, how are the questions and answers in the QA/Grounding/Spatial pairs generated? How to ensure the quality and diversity of the dataset?\n\n* In the training recipe, the authors adopt action chunks for robot control. An ablation over different chunk sizes would help motivate this design.\n\n* In Table 3, I do not see results for combining all in-domain data, yet line 419 claims gains from this combination. Please add the result or clarify.\n\n* A further limitation is the lack of real-robot experiments. While challenging, this should at least be acknowledged in the limitations section.", "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": "dOF2pMTlLr", "reviewer": "Reviewer_HWMv", "summary": "This paper proposes VLASER, a VLA model with collaborative embodied inference ability, which aims to solve the domain gap between upstream VLM inference and downstream VLA strategy learning. VLASER is based on InternVL3, including a two-stage training process of \"VLM pre-training + VLA fine-tuning\", and relies on the Vlaser-6M dataset to achieve SOTA performance on 12 embodied inference benchmarks (Vlaser-8B average score of 51.3), while in WidowX Achieve the best results on the robot benchmark (Vlaser-QA average success rate of 64.6%) and perform competitively on the Google Robot benchmark; The study also reveals key findings: out-of-domain embodied inference data has limited VLA performance improvement, while in-domain data based on robot interaction data can accelerate VLA convergence and improve task success rate.", "questions": "See Weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "See Summary", "weaknesses": "1. In Section 3.2, the authors said there is no positive correlation between common embodied reasoning benchmarks and the closed-loop control performance of robot-specific embodied subordinates, but there is a lack of corresponding quantitative analysis or visualization support.\n2. Figure 2 aims to show the two-stage training process of Vlaser, but the data flow and processing logic within each stage are not clear enough, and the relationship between the steps is unclear.\n3. The description of the training process in Section 2.3 is fuzzy. Although the authors describe the two-stage training strategy and the form of the core loss function, there is not enough detail about the mathematical mechanism of key processes such as flow matching action generation. The relevant parameters only give fixed values, but do not explain their physical meaning or selection basis.\n4. The paper mentions that the VLA data in 2 million domains is generated by Qwen2.5VL-7B, but does not explain how to control the data quality after the generation, and suggests supplementary data quality evaluation methods.\n5. The paper points out that \"in-domain data improves VLA performance\", but does not clarify the separate contributions of general QA, grounding QA, and spatial inference QA in in-domain data.\n6. The title of the paper mentions synergistic embodied reasoning, but it is not clear where the synergy is reflected.", "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)