Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning

## Abstract

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.
The code, model and data are available at https://github.com/OpenGVLab/Vlaser/ .

## 1 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-language-action (VLA) models. Developing foundation models that possess strong reasoning and control capabilities is therefore an important advancement toward general-purpose embodied AI.

In this context, vision-language models (VLMs) (OpenAI, 2023; Liu et al., 2023; Chen et al., 2024; Bai et al., 2025; Team et al., 2023) emerge as natural candidates to 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 an embodied agent in grounding, planning, and spatial reasoning.
Meanwhile, a significant body of work extends vision-language models (VLMs) into vision-language-action models (VLAs) (Kim et al., 2024; Intelligence et al., 2025; Driess et al., 2025) for robot control.
While there are some approaches (Intelligence et al., 2025; Driess et al., 2025) that demonstrate the effectiveness of co-training with web data for the generalization 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 to construct Vlaser, an embodied vision-language model that possesses strong embodied reasoning capabilities, and subsequently answer this question based on the corresponding vision-language-action 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 on planning-based control (Huang et al., 2022; Gasparetto et al., 2015; Zhang et al., 2018), which requires a strong foundational ability in grounding and planning. Planning and Grounding are cornerstones of the agents embodied in the physical world. Meanwhile, spatial understanding increasingly attracts the interest of the community in addressing the spatial perception ability of VLM. To this end, we firstly aim to introduce an 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 [1](https://arxiv.org/html/2510.11027v1#S1.F1), the resulting dataset spans a 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-art performance across a variety of embodied reasoning benchmarks, demonstrating strong generalization in both open-loop inference and closed-loop control settings.

Figure: 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—along with in-domain simulation-sourced pairs. Top-right: A LiDAR visualization illustrating the state-of-the-art embodied reasoning capability of the Vlaser VLM. Bottom-left: The pre-trained Vlaser VLM significantly accelerates convergence in downstream Vision-Language Action model (VLA) policy learning on WidowX platform (Walke et al., 2023a). Bottom-right: Successful closed-loop operation of an agent powered by Vlaser within the SimplerEnv benchmark (Li et al., 2024b).
Refer to caption: x1.png

Existing Vision-Language-Action (VLA) models (Black et al., 2024; Cheng et al., 2024; Kim et al., 2024; Intelligence et al., 2025) 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 under-explored research problem.
To address this gap, we systematically investigate the VLM-to-VLA adaptation paradigm using our enhanced embodied vision-language model and associated data engine. Our experiments reveal an important insight: although out-of-domain embodied reasoning data significantly improve upstream reasoning capabilities as measured by standard benchmarks, these gains may not translate directly or prominently to downstream VLA performance. In contrast, in-domain data – annotated directly on robot interaction datasets such as Open X-Embodiment (O’Neill et 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-language model construction: It is urgent to shrink the domain gap between current embodied perception and reasoning benchmarks to the real-world embodied tasks, and thus facilitate the 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-language model that enhances InternVL 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 vision-language pretraining data contribute most effectively to downstream Vision-Language-Action (VLA) policy learning. Our findings offer practical insights for constructing task-aware data streams that bridge the gap between Internet-scale pretraining and embodied-specific fine-tuning.

State-of-the-art performance across embodied benchmarks. Among models of comparable scale, Vlaser achieves top-tier results on a comprehensive set of embodied reasoning benchmarks—spanning visual grounding, task planning, spatial reasoning, and simulation-based robot evaluation, demonstrating its strong generalization and applicability to both open-loop inference and closed-loop control scenarios.

## 2 Method

Figure: Figure 2: An illustration of Vlaser architecture. Vlaser includes two components and corresponding training phases: 1) the Multimodal Pretraining is for embodied reasoning enhancement based on the corresponding data engine; 2) VLA training is performed on the action expert module, which handles low-level control based on flow matching action generation.
Refer to caption: x2.png

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 [2.1](https://arxiv.org/html/2510.11027v1#S2.SS1). Then, we illustrate the data engine in Section [2.2](https://arxiv.org/html/2510.11027v1#S2.SS2). Section [2.3](https://arxiv.org/html/2510.11027v1#S2.SS3) discusses the training recipe that includes embodied reasoning pretraining and vision-language-action finetuning.

### 2.1 Model Structure

The structure of  Vlaser consists of two major components: the typical vision-language backbone (Chen et al., 2024; Liu et al., 2023) and the action expert for low-level control, as shown in Figure [2](https://arxiv.org/html/2510.11027v1#S2.F2). We illustrate the two components respectively in this section.

VLM Backbone Vision-language models (VLMs) are key candidates for embodied agents, providing both perception and reasoning abilities. Vlaser , built on InternVL3 (Zhu et al., 2025), integrates embodied reasoning with robot control for embodied agents. While InternVL3 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 InternViT (Chen et 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 a large number of MLLMs (Team et al., 2025a; NVIDIA et al., 2025a) that enhance the ability of embodied common-sense reasoning for agents, while a few approaches equip the embodied MLLMs with end-to-end robot control. Vlaser extends the MLLMs with a low-level robot control and verifies the capability of different data streams in downstream VLA finetuning. Following (Intelligence et al., 2025), we design an action expert based on the open-source vision-language model (Chen et al., 2024; Zhu et al., 2025). Meanwhile, we utilize the flow matching (Lipman et al., 2023a) for action prediction based on the llava-like vision-language structure, while sharing the self-attention 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 action expert. Meanwhile, we utilize non-causal attention for the VLA stream. During inference, we 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](https://arxiv.org/html/2510.11027v1#A1.SS2).

Embodied Grounding Data
The Vlaser dataset incorporates two distinct 2D grounding formats—bounding boxes and center points—both normalized to the range [0, 1000] to ensure consistent and resolution-invariant grounding predictions across diverse image resolutions. Specifically, we collect 1.5 million high-quality question-answer pairs that support multiple grounding tasks: predicting bounding boxes from open-vocabulary descriptions, localizing object center points based on textual descriptions, and identifying objects from given spatial coordinates.
The data is sourced from several open embodied grounding datasets, including RoboPoint (Yuan et al., 2024), ShareRobot (Ji et al., 2025), Pixmo-Points (Deitke et al., 2025), Paco-LaVIS (Ramanathan et al., 2023), 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-1B dataset (Kirillov et al., 2023). 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 to 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 in general state perception and 3D spatial reasoning.
For the RoboVQA 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 et al., 2025a), among others. To support spatial understanding and reasoning, we incorporate datasets such as SPAR (Zhang et al., 2025), SpaceR-151k (Ouyang et al., 2025), and VILASR (Wu et al., 2025). Furthermore, we augment these with 100k manually annotated spatial understanding samples generated from publicly available 3D scene datasets—including ScanNet (Dai et al., 2017), ScanNet++ (Yeshwanth et al., 2023), CA-1M (Lazarow et al., 2025), and ARKitScenes (Baruch et 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, it is essential to decompose them into manageable sub-tasks and solve them step by step. This capability is commonly referred to 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-15k-Instruction (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., 2025). To improve the model’s ability to comprehend complex instructions and execute tasks, we followed the annotations of LLaRP (Szot et al., 2024) 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 as EgoPlan-IT (Chen et al., 2023) and EgoCOT (Mu et 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 are 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., 2024c). Within SimplerEnv, data is generated for two distinct robotic embodiments: the Google Robot (Brohan et al., 2023b; a; O’Neill et al., 2024) and the WidowX Robot (Walke et al., 2023a), ensuring broad morphological and kinematic coverage.
The question-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 each of the in-domain data in simulation is described in Appendix [A.2](https://arxiv.org/html/2510.11027v1#A1.SS2).

### 2.3 Training Recipe

Vlaser adopts a two-stage training recipe, designed to 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.

Vision-Language Pretraining
Vlaser is developed by supervised fine-tuning (SFT) InternVL3 (Zhu et al., 2025) on embodied-related datasets, including those focused on grounding, planning, and spatial intelligence. In the first training phase, we fine-tune InternVL3 using auto-regressive language modeling loss. In particular,
given the input images $x\in\mathbb{R}^{t\times h\times w\times 3}$ and textual prompt $y\in\mathbb{R}^{l}$, the language modeling loss $\mathcal{L}_{lm}$ can be defined by

$$ $\displaystyle\mathcal{L}_{lm}=-\log p(t_{N}|\mathcal{F_{\text{v}}}(x;\theta_{v}),\mathcal{F_{\text{t}}}(y),t_{0:N-1};\Theta),$ (1) $$

where $p\in\mathbb{R}^{m}$ is the next-token probability and $m$ denotes the vocabulary size. Here, $\mathcal{F_{\text{v}}}(\cdot)$ denotes the ViT and the MLP, and $\theta_{v}$ is their parameters. $\mathcal{F_{\text{t}}}(\cdot)$ is the textual tokenizer. $\Theta$ are the parameters of the LLM. $t_{i}$ denotes the i-th predicted word.

Vision-Language-Action Finetuning
For robot policy learning, we optimize the model using an action expert trained on robot-specific datasets. Vlaser integrates a flow-matching-based action expert to predict a sequence of future actions from a single-frame observation. Specifically, denote the action chunk $\mathbf{A}_{t}=[a_{t},a_{t+1},\dots,a_{t+H-1}]$, where $a_{t}$ represents the action in the current timestep $t$ and $H$ represents the action horizon. Meanwhile, we encode each noisy action with an action encoder (i.e., an MLP projector) as a single token for the action expert. For the action chunk, $\mathbf{A}_{t}^{\tau}=\tau\mathbf{A}_{t}+(1-\tau)\epsilon$ is the corresponding noisy action chunk, and we train the network to output $\mathbf{v}_{\theta}(\mathbf{A}^{\tau}_{t},\mathbf{o}_{t})$ to match the denoising vector field $\mathbf{u}(\mathbf{A}^{\tau}_{t}|\mathbf{A}_{t})=\epsilon-\mathbf{A}_{t}$, where $\mathbf{o}_{t}$ indicates the observations (e.g., image camera and robot state) at action timestep $t$, $\theta$ represents the network and $\tau\in[0,1]$ represents the flow matching timesteps. Therefore, the VLA optimization loss is as follows,

$$ $\mathcal{L}_{vla}=\left\|\mathbf{v}_{\theta}(A_{t}^{\tau},\mathbf{o}_{t})-\mathbf{u}(A_{t}^{\tau}|A_{t})\right\|^{2}$ (2) $$

We sample the action chunks from the robot episodes and flow-matching timesteps to optimize the network. At inference, we generate actions by integrating the
learned vector field from $\tau=0$ to $\tau=1$, starting with random
noise $\mathbf{A}^{0}_{t}\sim\mathcal{N}(\mathbf{0},\mathbf{I})$, as follows,

$$ $\mathbf{A}^{\tau+\delta}_{t}=\mathbf{A}^{\tau}_{t}+\delta\mathbf{v}_{\theta}(\mathbf{A}^{\tau}_{t},o_{t})$ (3) $$

where $\delta$ is the integration step size. In our experiments, we set $H$ as 4, and $\delta$ as 10.
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; Brohan et al., 2023b) 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

**Table 1: 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 normalized average performance of all the benchmarks. The best, second best and third best score among all the baselines are colored in red, orange and yellow.**
| Model | QA | Planning | Embodied Grounding | Spatial Intelligence | Simulation | Avg |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ERQA | Ego-Plan2 | Where2place | Pointarena | Paco-Lavis | Pixmo-Points | VSIBench | RefSpatial | MMSIBench | VLABench | EB-ALFRED | EB-Habitat |  |  |
| $\blacktriangledown$ *Closed-source MLLMs:* |  |  |  |  |  |  |  |  |  |  |  |  |  |
| GPT-4o-20241120 | 47.0 | 41.8 | 29.1 | 29.5 | 16.2 | 10.8 | 42.5 | 8.8 | 30.3 | 39.3 | 56.3 | 59.0 | 34.2 |
| Claude-3.7-Sonnet | 35.5 | 41.3 | 25.6 | 22.2 | 12.4 | 7.2 | 47.0 | 7.7 | 30.2 | 41.7 | 67.0 | 65.7 | 33.6 |
| Gemini-2.5-Pro | 55.0 | 42.9 | 39.9 | 62.8 | 45.5 | 25.8 | 43.4 | 30.3 | 36.9 | 34.8 | 62.7 | 53.0 | 44.4 |
| $\blacktriangledown$ *Small Size MLLMs:* |  |  |  |  |  |  |  |  |  |  |  |  |  |
| ChatVLA-2B | 34.3 | 25.3 | 3.7 | 10.1 | 10.2 | 2.1 | 2.4 | 0.9 | 20.1 | 0.0 | 0.0 | 0.0 | 9.1 |
| InternVL3-2B | 31.5 | 30.9 | 5.2 | 7.1 | 15.4 | 1.4 | 31.5 | 1.8 | 25.3 | 19.4 | 1.3 | 12.0 | 15.2 |
| Qwen2.5VL-3B | 35.3 | 30.3 | 31.0 | 41.7 | 67.4 | 36.6 | 27.9 | 24.9 | 26.5 | 31.3 | 6.7 | 19.7 | 31.6 |
| Embodied-R1-3B | 36.0 | 36.0 | 35.1 | 45.3 | 68.3 | 36.6 | 28.0 | 28.5 | 26.0 | 24.6 | 7.0 | 19.3 | 32.5 |
| RoboBrain2.0-3B | 37.3 | 41.8 | 64.2 | 46.0 | 67.6 | 36.9 | 28.8 | 46.5 | 26.8 | 18.1 | 0.0 | 10.0 | 35.3 |
| Vlaser-2B | 35.8 | 38.3 | 74.0 | 57.8 | 72.5 | 44.6 | 57.5 | 43.0 | 23.6 | 23.1 | 42.3 | 30.7 | 45.3 |
| $\blacktriangledown$ *Medium Size MLLMs:* |  |  |  |  |  |  |  |  |  |  |  |  |  |
| Magma-8B | 29.3 | 27.9 | 10.9 | 29.6 | 15.3 | 10.1 | 12.7 | 4.5 | 26.2 | 8.5 | 0.0 | 0.0 | 14.6 |
| Cosmos-Reason1-7B | 39.3 | 26.9 | 11.4 | 40.8 | 61.8 | 23.6 | 33.9 | 5.4 | 26.4 | 35.5 | 4.0 | 5.3 | 26.2 |
| VeBrain-7B | 38.3 | 27.3 | 33.1 | 38.9 | 55.1 | 20.1 | 39.9 | 20.6 | 28.3 | 25.9 | 5.7 | 12.3 | 28.8 |
| InternVL3-8B | 35.3 | 40.0 | 10.0 | 14.2 | 21.1 | 5.7 | 42.1 | 5.6 | 25.7 | 24.7 | 19.0 | 23.7 | 22.3 |
| Qwen2.5VL-7B | 39.3 | 29.7 | 31.1 | 56.3 | 68.0 | 43.5 | 38.2 | 32.1 | 25.9 | 36.4 | 10.0 | 18.3 | 35.7 |
| Embodied-R1-7B | 38.3 | 37.1 | 69.5 | 51.2 | 69.9 | 39.2 | 38.6 | 31.1 | 28.1 | 35.5 | 10.0 | 19.0 | 38.9 |
| RoboBrain2.0-7B | 42.0 | 33.2 | 63.6 | 49.5 | 73.1 | 37.8 | 36.1 | 32.5 | 26.5 | 6.6 | 14.0 | 29.3 | 37.0 |
| Vlaser-8B | 41.0 | 53.4 | 69.5 | 60.3 | 68.3 | 40.5 | 60.3 | 59.2 | 27.2 | 45.6 | 50.0 | 40.0 | 51.3 |

Evaluation Datasets
We conduct a comprehensive evaluation of embodied reasoning capabilities across a 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., 2025b), Ego-Plan2 (Qiu et al., 2024), Where2place (Yuan et al., 2024), Pointarena (Cheng et al., 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), MMSI-Bench (Yang et 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 et al., 2020) and Habitat (Szot et al., 2021).

Baselines
Since our method, Vlaser is trained at two model scales – 2B and 8B parameters, we categorize the compared baseline methods into three groups for a systematic evaluation: 1) State-of-the-art closed-source models, including GPT-4o (OpenAI, 2025), Claude-3.7-Sonnet (Anthropic, 2025), and Gemini-2.5-Pro (Comanici et al., 2025); 2) Small-scale MLLMs (2B – 3B parameters), comprising ChatVLA-2B (Zhou et al., 2025b), InternVL3-2B (Zhu et al., 2025), Qwen2.5-VL-3B (Bai et al., 2025), Embodied-R1-3B (Yuan et al., 2025), and RoboBrain2.0-3B (Team et al., 2025a); 3) Medium-scale MLLMs (7B – 8B parameters), including Magma-8B (Yang et al., 2025a), Cosmos-Reason1-7B (NVIDIA et al., 2025a), VeBrain-7B (Luo et al., 2025), InternVL3-8B (Zhu et al., 2025), Qwen2.5-VL-7B (Bai et al., 2025), Embodied-R1-7B (Yuan et al., 2025), and RoboBrain2.0-7B (Team et al., 2025a).

The overall experimental results are presented in Table [1](https://arxiv.org/html/2510.11027v1#S3.T1). As shown in Table [1](https://arxiv.org/html/2510.11027v1#S3.T1), compared to the base models InternVL3-2B and InternVL3-8B 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 chain-of-thought (CoT) reasoning. This scaling behavior indicates the importance of appropriate model size selection based on target application requirements.

When compared against current state-of-the-art embodied-specific VLMs, including RoboBrain2.0 (Team et al., 2025a) and Embodied-R1 (Yuan et 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 a well-balanced and robust capability set, performing strongly across multiple dimensions of embodied intelligence – from embodied question answering and state estimation to 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, we 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.

### 3.2 Performance on downstream Close-Loop Robot Tasks

Finetuning Datasets We conduct extensive experiments on SimplerENV to evaluate the performance of Vlaser  and  Vlaser data engine on closed-loop robotic manipulation tasks. SimplerENV is an open-source suite of purpose-built simulated environments with nearly 150K episodes for evaluating real-world robot manipulation policies in a scalable, reproducible way. It targets the key real-to-sim gaps – control and vision so that simulated performance reliably tracks real-robot outcomes. Across Google Robot and WidowX/BridgeData V2 setups, SimplerEnv reports strong real-vs-sim correlations and faithfully reflects behavior under distribution shifts, enabling 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.

**Table 2: SimplerEnv Evaluation on WidowX Robot Tasks. Avg indicates the average success rate among the four tasks. InternVL3-2B, Vlaser and Vlaser with Bridge Q&A indicates the base model we select to fine-tune on the WidowX Robot Tasks. Particularlly, Vlaser-QA refers to the model which is fine-tuned on the Bridge question-answer pairs dataset. Model sizes are indicated within parentheses. The result of RT-1-X (Brohan et al., 2023b), Octo-Base (Team et al., 2024), OpenVLA (Kim et al., 2024), RoboVLM (Liu et al., 2025) and SpatialVLA (Qu et al., 2025b) are from (Qu et al., 2025b) while the results of $\pi_{0}$ (Black et al., 2024) is from (open-pi zero, 2025).**
| Model | Carrot on plate | Put eggplant in 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% |
| OpenVLA (7B) (OpenAI, 2023) | 0% | 4.1% | 0% | 0% | 1.0% |
| RoboVLM (2B) (Liu et al., 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% |
| $\pi_{0}$ (3B)  (Black et al., 2024) | 55.8% | 79.2% | 63.3% | 21.3% | 54.9% |
| InternVL3-2B | 42.9% | 57.1% | 55.8% | 11.3% | 41.8% |
| Vlaser (2B) | 60.8% | 35.4% | 56.7% | 20.0% | 43.2% |
| Vlaser-QA (2B) | 55.8% | 83.3% | 77.9% | 33.3% | 64.6% |

**Table 3: Comparison with existing methods in SimplerEnv on Google Robot tasks. Avg indicates the average success rate among the three tasks. In the last five lines in the table, we use the name of base model to indicate different evaluation settings. Vlaser-QA indicates the model which is fine-tuned on the Fractal question-answer pair dataset. Vlaser-Spatial and Vlaser-Grounding represent the model fine-tuned on the Spatial Reasoning Data and Embodied Grounding Data separately. The details of different sub-datasets can be find in the Appendix[A.2](https://arxiv.org/html/2510.11027v1#A1.SS2). Model sizes are indicated within parentheses. The results of TraceVLA (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., 2025), Emma-X (Sun et al., 2024), Magma (Yang et al., 2025a), GR00T N1.5(NVIDIA et al., 2025b) and $\pi_{0}$ (Black et al., 2024) are from (Lee et al., 2025).**
| Model | Visual Matching | Avg | Variant Aggregation | Avg |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Pick Coke Can | Move Near | Drawer | Pick Coke Can | Move Near | Drawer |  |  |  |
| TraceVLA (7B) (Zheng et al., 2024) | 28.0% | 53.7% | 57.0% | 42.0% | 60.0% | 56.4% | 31.0% | 45.0% |
| RT-1-X (35M) (Brohan et al., 2023b) | 56.7% | 31.7% | 59.7% | 53.4% | 49.0% | 32.3% | 29.4% | 39.6% |
| Octo-Base (93M) (Team et al., 2024) | 17.0% | 4.2% | 22.7% | 16.8% | 0.6% | 3.1% | 1.1% | 1.1% |
| OpenVLA (7B) (Kim et al., 2024) | 16.3% | 46.2% | 35.6% | 27.7% | 54.5% | 47.7% | 17.7% | 39.8% |
| RoboVLM (2B) (Liu 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% |
| GR00T N1.5 (2.1B) (NVIDIA et al., 2025b) | 69.3% | 68.7% | 35.8% | 52.4% | 46.7% | 62.9% | 17.5% | 43.7% |
| $\pi_{0}$ (3B) (Black et al., 2024) | 72.7% | 65.3% | 38.3% | 58.3% | 75.2% | 63.7% | 25.6% | 54.8% |
| InternVL3-2B | 94.3% | 78.8% | 19.0% | 64.0% | 80.4% | 72.7% | 11.1% | 54.7% |
| Vlaser(2B) | 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% |
| Vlaser-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% |

Baselines As mentioned before, we integrate Vlaser with an action expert utilizing flow matching for low-level control fine-tuning and evaluating the model on the WidowX and Google Robot datasets from SimplerEnv. We conduct experiments using InternVL3-2B, Vlaser, and the models based on Vlaser data engine for the in-domain robot data. Commonly, embodied reasoning can be marginally categorized into embodied QA (including planning), embodied grounding, and embodied spatial intelligence. We thus construct corresponding embodied VLMs based on the corresponding data stream:  Vlaser-QA,  Vlaser-Grounding,  Vlaser-Spatial. Notably, all series of  Vlaser models are based on the same architecture with 2B size.

The full experimental results are presented in Table [2](https://arxiv.org/html/2510.11027v1#S3.T2) and Table [3](https://arxiv.org/html/2510.11027v1#S3.T3). We observe the proposed vision-language-action architecture based on InternVL3-2B is capable of low-level robot control and achieves comparable performance to many previous approaches, though we utilize a 2B backbone.

Particularly, we did not observe any clean improvement when we used the  Vlaser-2B as our initial backbone on both WidowX and Google Robot, while we achieved significant performance promotion with the  Vlaser-QA, although the architecture and model size are the same between the two models. This observation illustrates the effectiveness of our Vlaser data engine, and meanwhile identifies that there is no 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 is urgent to shrink the domain gap between the foundational models and real-world robot embodiment for closed-loop task completion.

In addition, we conducted a simple ablation study on the Google Robot Tasks to evaluate the effectiveness of different data annotations. The experimental results indicate that incorporating all types of data leads to significant improvements, achieving performance comparable to the baseline. We attribute these improvements primarily to the reduction of the vision observation domain shift.

## 4 Related Work

Vision-Language Model for Embodied Reasoning
Enhancing the embodied reasoning capabilities of current state-of-the-art Vision-Language Models (VLMs) has emerged as a critical research direction. These capabilities encompass a 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 as 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-language model (VLM), demonstrating strong generalization across a variety of tasks such as 3D scene perception, visual pointing, state estimation, and affordance prediction. In parallel, a number of data-driven methodologies have emerged to support such reasoning capabilities. For instance, Cosmos-Reason1 (NVIDIA et al., 2025a), VeBrain (Luo et al., 2025), MolmoAct (Lee et al., 2025), and EmbodiedOneVision (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 visual-language alignment.
Further advancing this direction, several frameworks – including the RoboBrain series (Ji et al., 2025; Team et al., 2025a), Embodied R1 (Yuan et al., 2025), and Robix (Fang et al., 2025) 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-horizon 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-Action models.
Developing a generalist model remains a central challenge in robotics. Inspired by the strong generalization abilities of vision-language models (VLMs) (OpenAI, 2023; Chen et al., 2024; Bai et al., 2025; Team et al., 2023) trained on large-scale internet data, researchers have proposed vision-language-action (VLA) models, which have demonstrated promising performance (Brohan et al., 2023a; Kim et al., 2024; Qu et al., 2025b; Hou et al., 2025). Compared to traditional robot policies, VLA models are 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 et al., 2022; Peebles & Xie, 2023), flow matching (Lipman et al., 2023b), and mixture-of-experts (MoE) (Shazeer et al., 2017) into VLA models, and have adopted larger, more capable VLMs as their backbones. These advances have enabled VLA models to tackle a wider range of complex real-world manipulation tasks.
Nevertheless, current VLA models remain limited in their generalization. In particular, they have not yet reached the level of general reasoning exhibited by VLMs, such as decompose a complex task into manageable sub-steps and complete the task in a zero-shot manner.
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., 2025; Driess et al., 2025; Zhou et al., 2025b) explore unified training frameworks for VLMs and VLAs to 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 as spatial understanding, grounding, or planning and which types of training data most effectively enhance the control capabilities of a VLA model.
In this work, we take an 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.

## 5 Conclusion and Discussion

We introduce Vlaser, a foundational vision-language-action 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 a 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 a 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 perspective, despite the abundance of vision datasets available. Therefore, we argue that it is essential to develop alignment techniques to bridge the domain gap in representations between the robot’s viewpoint and that of internet datasets.

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## Appendix A Appendix

### A.1 Training Details

Our Vlaser is optimized in a fully supervised finetuning (SFT) manner based on InternVL3 series (Zhu et al., 2025)(InternVL3-2B and InternVL3-8B). To maximize adaptation to 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 [4](https://arxiv.org/html/2510.11027v1#A1.T4).

**Table 4: 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 | AdamW |
| Optimizer hyperparameters | $\beta_{1}=0.9$, $\beta_{2}=0.999$, $eps=1e-8$ |
| Peak learning rate | 2e-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 | bfloat16 |

**Table 5: Hyper-parameters used in the VLA fine-tuning.**
| Configurations | Values |
| --- | --- |
| LLM sequence length | 384 |
| Image Size | 448 |
| Freeze VLM | False |
| Global batch size | 1024 |
| Training epochs | 10 |
| VLM Peak Learning Rate | 5e-5 |
| Action Expert Peak Learning Rate | 5e-5 |
| Optimizer | AdamW |
| Optimizer hyperparameters | $\beta_{1}=0.9$, $\beta_{2}=0.999$, $eps=1e-8$ |
| Observation history length | 1 |
| Action Chunk length | 4 |
| Execute Action length | 2 |

While using  Vlaser  as the base model for downstream VLA Policy fine-tuning, we optimize all parameters within both the VLM and the Action Expert. Additionally, we conduct comparative experiments using several different versions of base models, including InternVL3-2B, etc. Detailed information and related parameter settings can be found in Table [5](https://arxiv.org/html/2510.11027v1#A1.T5).

### 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 et al., 2023). The 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 a coordinate within the mask region. To ensure annotation quality, we apply an IoU 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 1 million candidate instances.
In the second stage, we employ a 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-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 a dataset of 100k 3D spatial perception samples derived from ScanNet (Dai et al., 2017), ScanNet++ (Yeshwanth et al., 2023), and ARKitScenes (Baruch et al., 2021). Following methodologies established in prior data engines (Deng et al., 2025; Fan et al., 2025), we utilize both the 3D point cloud and video sequences of each scene to construct a 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 et al., 2021).
Specifically, we initialize each planning task following the annotations of LLaRP (Szot et al., 2024), which specify both the task goals and the set of permissible actions.
An LLM agent based on gpt-4o (OpenAI, 2025) is then deployed to roll out the task.
During each rollout, we record the task instruction, the sequence of actions taken, and the environment’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 a complex environment.

In-Domain Data for downstream VLAs
We generate 2 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 robotic platforms: the WidowX Robot (Walke et al., 2023a) and the Google Robot (Brohan et al., 2023b; a) within the SimplerEnv (Li et al., 2024c) simulation framework.
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 as the actionable affordances; 3) Spatial Reasoning 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 Figure [3](https://arxiv.org/html/2510.11027v1#A1.F3) (General QA), Figure [4](https://arxiv.org/html/2510.11027v1#A1.F4) (Grounding QA), and Figure [5](https://arxiv.org/html/2510.11027v1#A1.F5) (Spatial Reasoning QA), respectively. We use Qwen2.5VL-7B (Bai et al., 2025) as the base model to generate the above data items.

### A.3 Simulation Evaluation Details

We fine-tune and evaluate the VLA models using various base models within the SimplerEnv simulation environment. To ensure fair evaluation, we use checkpoints with the same number of iterations for the WidowX Robot Task and the Google Robot Task, respectively. Specifically, for the WidowX Robot Tasks, we use a 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 10 inference steps during the inference phase and apply Euler method as numerical integration method. We evaluate a sufficient number of samples to ensure the reliability of the tests. The exact number of test samples for each task is shown in the Table [6](https://arxiv.org/html/2510.11027v1#A1.T6).

**Table 6: The number of samples evaluated on SimplerEnv.**
|  |  | Task Name | Evaluation Samples |
| --- | --- | --- | --- |
| Widowx Robot Tasks | Carrot on plate | 240 |  |
| Put eggplant in basket | 240 |  |  |
| spoon on towel | 240 |  |  |
| stack cube | 240 |  |  |
| Google Robot Tasks | Visual Matching | Pick coke can | 300 |
| Move Near | 240 |  |  |
| Open/Close Drawer | 216 |  |  |
| Variance Aggregation | Pick coke can | 825 |  |
| Move Near | 600 |  |  |
| Open/Close Drawer | 378 |  |  |

**Table 7: Embodied QA examples.**
| Embodied QA: Example #1 from ERQA. |  |
| --- | --- |
|  |  |
| Question: | What happened between these two frames? Choices: A. Robot arm lifted the cup. B. Robot arm poured all the nuts into a 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. |

**Table 8: Embodied Grounding Examples.**
| Embodied Grounding: Example #1 from PointArena. |  |
| --- | --- |
|  |  |
| Question: | Point to the tool people use for writing. |
| Vlaser-8B: | <point>[[701, 374]]</point>. |
| Embodied Grounding: Example #2 from Where2Place. |  |
|  |  |
| Question: | Please point out the free space between the black water bottle and the pot lid. |
| Vlaser-8B: | <point>[[293, 560]]</point>. |

**Table 9: Spatial Intelligence Examples.**
| Spatial Reasoning: Example #1 from VSI. |  |  |  |
| --- | --- | --- | --- |
|  |  |  |  |
|  |  |  |  |
| Question: | You are 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 fill in], choose either ‘turn 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. |  |  |
| Vlaser-8B: | B. |  |  |

**Table 10: Simulation planning Examples.**
| Simulation Planning: Example #1 from EmbodiedBench. |  |  |  |  |
| --- | --- | --- | --- | --- |
|  |  |  |  |  |
|  |  |  |  |  |
| Question: | You are a robot operating in a home. Given a task, you must accomplish the task using a defined set of actions to achieve the desired outcome.<br>Now the human instruction is: Put a microwaved tomato slice on the counter.<br>You need to output the reasoning process and an action to execute. |  |  |  |
| Vlaser-8B: | Reasoning-step-1: To put a microwaved tomato slice on the counter, I need to start by slicing a tomato. To do this, I require a 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 I have the knife, I can proceed with slicing the tomato. Therefore, my next action is to find the knife. Action: find a knife.<br>Reasoning-step-2: I have identified the knife on the counter near the sink. Picking up the knife is the next logical step because it is necessary for slicing the tomato. Once I have the knife, I can proceed with cutting the tomato into slices. This aligns with the goal of preparing a tomato slice for microwaving and then placing it on the counter. Action: pick up the knife.<br>Reasoning-step-3: I have successfully located the knife in the drawer. Now, I need to find a tomato to slice. The image shows a kitchen counter with various items, but no tomato is immediately visible. Tomatoes are commonly stored in the refrigerator or on the counter. Since the refrigerator is partially visible on the left side of the image, it makes sense to check there first. If the tomato isnẗ in the fridge, I can then look around the counter more thoroughly. Therefore, my next step is to find a tomato. Action: find a tomato.<br>…<br>Reasoning-step-9: The current visual state shows the microwave with its door closed, and the tomato slice is on the counter. The task is to 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 to confirm its placement by finding a suitable counter top. This ensures that the tomato slice is securely and appropriately positioned for the task. Therefore, the next step is to find a counter top. Action: find a counter top.<br>Reasoning-step-10: The current visual state shows a 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 is 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 is to put down the tomato. Action: put down the tomato. |  |  |  |

### 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 [7](https://arxiv.org/html/2510.11027v1#A1.T7),
grounding [8](https://arxiv.org/html/2510.11027v1#A1.T8), spatial intelligence [9](https://arxiv.org/html/2510.11027v1#A1.T9) and planning tasks in Simulation [10](https://arxiv.org/html/2510.11027v1#A1.T10).
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 [6](https://arxiv.org/html/2510.11027v1#A1.F6) and Figure [7](https://arxiv.org/html/2510.11027v1#A1.F7) present qualitative examples from the evaluations conducted in the SimplerEnv. Specifically, Figure [6](https://arxiv.org/html/2510.11027v1#A1.F6) showcases the performance of multiple models on the WidowX Robot Task, while Figure [7](https://arxiv.org/html/2510.11027v1#A1.F7) demonstrates 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, our model shows significant improvements in this regard. Whether incorporating in-domain question-answer pair data, spatial intelligence data, or grounding data, the performance of the model shows clear enhancement. These results validate the effectiveness of our approach.

Figure: Figure 3: An illustration of Vlaser-6M data engine for in-domain general QA sample in SimplerEnv.
Refer to caption: x3.png

Figure: Figure 4: An illustration of Vlaser-6M data engine for in-domain embodied grounding QA sample in SimplerEnv.
Refer to caption: x4.png

Figure: Figure 5: An illustration of Vlaser-6M data engine for in-domain spatial reasoning QA sample in SimplerEnv.
Refer to caption: x5.png

Figure: Figure 6: Qualitative samples in SimplerEnv on WidowX Robot Tasks
Refer to caption: x6.png

Figure: Figure 7: Qualitative samples in SimplerEnv on Google Robot Tasks
Refer to caption: x7.png

## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
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?

2. 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.
soundness: 3
strengths: 1. The paper is well-written, with clear figures and detailed explanations that make the work easy to follow.

2. 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.

3. The paper demonstrates state-of-the-art performance across 12 embodied-reasoning benchmarks and consistently surpasses strong baselines.

4. It also conducts a systematic analysis of pretraining data effectiveness for VLA transfer and offers scaling insights.
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.
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.

2. 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.

---

confidence: 3
contribution: 3
initial_rating: 4
presentation: 3
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.
2. Although the paper describes the training and inference procedures, it is still unclear what exactly the pretrained VLM passes to the VLA.
3. In Figure 1, why are the closed-source models not included in the ranking?
4. Explain what is the open-loop inference and closed-loop control?
5. After adding the reasoning capability, does it affect the runtime efficiency of the VLA? Is this reasoning capability truly cost-effective?
soundness: 3
strengths: 1. The paper presents a unified framework for embodied tasks and introduces the Vlaser-6M dataset.
2. The dataset is large, diverse, and carefully curated, covering grounding, spatial reasoning, planning, and simulation tasks.
3. The study offers useful observations on the gap between vision-language reasoning and low-level control, providing practical guidance for future model design.
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.
weaknesses: 1. The detailed architecture of the action expert is not presented. In Figure 2, the structure of the action expert is not shown.
2. I think the authors could provide more explanations or figures to help readers understand the structure of the action expert and the training process.
3. 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.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: Please refer to the weakness part.
soundness: 3
strengths: * The paper is clearly written and easy to follow.
* This paper tackles an important problem: bridging VLMs and VLAs via embodied-data pretraining and in-domain fine-tuning.
* Results on embodied reasoning benchmarks and SimplerEnv are promising.
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.
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.

* 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.

* 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?

* In the training recipe, the authors adopt action chunks for robot control. An ablation over different chunk sizes would help motivate this design.

* 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.

* A further limitation is the lack of real-robot experiments. While challenging, this should at least be acknowledged in the limitations section.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: See Weakness
soundness: 2
strengths: See Summary
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.
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.
2. 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.
3. 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.
4. 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.
5. 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.
6. The title of the paper mentions synergistic embodied reasoning, but it is not clear where the synergy is reflected.


Decision: Accept (Poster)