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arxiv: 2606.09009 · v1 · pith:YTIYBM6Inew · submitted 2026-06-08 · 💻 cs.CV

Scaling by Diversified Experience for Vision-Language-Action Models

Leiyu Wang , Zhaofengnian Wang , Xueqi Li , Luoyi Fan , Cewu Lu , Nanyang Ye This is my paper

Pith reviewed 2026-06-27 17:11 UTC · model grok-4.3

classification 💻 cs.CV
keywords vision-language-action modelsintention decouplingreinforcement learningrobotic tasksout-of-distribution generalizationpolicy optimizationdiversified experiences
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The pith

SyVLA uses intention decoupling and guided reinforcement learning on diversified experiences to raise robotic task success and out-of-distribution generalization while keeping vision-language skills intact.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents SyVLA as a vision-language-action model trained on diversified experiences to overcome the mixing of high-level reasoning with low-level control and the instability of policy training. It introduces an Intention Decoupling algorithm that separates control-relevant features from reasoning contexts and a similar-sample guided RL pipeline that stabilizes updates and limits distribution shift. Experiments on real-world robotic tasks and multi-modal benchmarks show higher task success rates, stronger generalization to new conditions, and preserved vision-language performance relative to prior approaches.

Core claim

SyVLA is a VLA model trained with diversified experiences. It applies an Intention Decoupling algorithm to isolate control-relevant features from reasoning contexts and a similar-sample guided RL pipeline to stabilize policy updates and mitigate distribution shift, resulting in superior task success rates, stronger out-of-distribution generalization on robotic tasks, and maintained core vision-language capabilities.

What carries the argument

Intention Decoupling algorithm that isolates control-relevant features from reasoning contexts, combined with a similar-sample guided RL pipeline for stable policy updates.

If this is right

  • SyVLA records higher success rates on real-world robotic tasks than existing VLA methods.
  • The model shows stronger generalization to out-of-distribution conditions.
  • Core vision-language capabilities remain intact alongside gains in control.
  • Policy optimization becomes more stable with reduced distribution shift during training.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The separation of reasoning and control features may prove useful in other multimodal control settings that face similar entanglement.
  • Diversified experience collection could support scaling VLA training to more complex or longer-horizon tasks without proportional instability.

Load-bearing premise

The Intention Decoupling algorithm isolates control-relevant features from reasoning contexts and the similar-sample guided RL pipeline stabilizes updates without introducing new distribution issues.

What would settle it

If real-world robotic task success rates show no improvement or decline after applying the Intention Decoupling algorithm and similar-sample guided RL pipeline compared with baseline VLA models, the central performance claim would be falsified.

Figures

Figures reproduced from arXiv: 2606.09009 by Cewu Lu, Leiyu Wang, Luoyi Fan, Nanyang Ye, Xueqi Li, Zhaofengnian Wang.

Figure 1
Figure 1. Figure 1: We propose an Intention Decoupling algorithm to disentangle control-irrelevant high-level reasoning information in Feature Query Tokens, and develop an RL pipeline for stable real-world reinforcement learning. With these two methods, our SyVLA model achieves a balance between robotic task competence and visual-language understanding capability preservation. trained SyVLA can easily exhibit imprecise action… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our Intention Decoupling algorithm and our Similar Sample Guided RL pipeline. Left: To avoid degradation in the SyVLA’s action capability, we use a gradient-based identifier to find the tokens weakly associated with the control intention representation, and then mask these tokens’ last hidden state before feeding them into the Action Expert. Right: To stabilize our RL training, we select sample… view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of three tasks. The figure presents the scenes of three real-world robotic tasks and illustrates the process by which our SyVLA completes these tasks in a “Think-Before-Act” manner. the large-scale pretrained pi0 model, strongly demonstrating the effectiveness of our approach. In the out-of-distribution setting, our method significantly outperforms all baselines and suffers from the smallest … view at source ↗
Figure 4
Figure 4. Figure 4: The architecture of our RL infrastructure Reinforcement Learning In the reinforcement learning stage, we perform RL starting from the SyVLA model obtained in the previous stage. We build a value model of about 100M parameters with a Siglip encoder and a transformer network to predict the value of current state based on observation. To mitigate the prediction noise of the value model in early RL, we warm-up… view at source ↗
read the original abstract

Vision-Language-Action models face significant challenges in real-world deployment due to the entanglement of high-level reasoning with low-level control, and the instability of policy optimization. In this paper, we introduce SyVLA, a robust VLA model trained with diversified experiences. We propose an Intention Decoupling algorithm to isolate control-relevant features from reasoning contexts and a similar-sample guided RL pipeline to stabilize policy updates and mitigate distribution shift. Extensive experiments on real-world robotic tasks and multi-modal benchmarks demonstrate that SyVLA achieves superior task success rates and stronger out-of-distribution generalization compared to existing methods, while effectively preserving core vision-language capabilities. Codes and Datasets is released on \href{https://sy-vla.github.io/}{project page}.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper introduces SyVLA, a robust VLA model trained with diversified experiences. It proposes an Intention Decoupling algorithm to isolate control-relevant features from reasoning contexts and a similar-sample guided RL pipeline to stabilize policy updates and mitigate distribution shift. Extensive experiments on real-world robotic tasks and multi-modal benchmarks are claimed to demonstrate superior task success rates, stronger out-of-distribution generalization compared to existing methods, while preserving core vision-language capabilities. Codes and datasets are released.

Significance. If the experimental claims hold, this would represent a meaningful advance for vision-language-action models by directly targeting the entanglement of high-level reasoning with low-level control and the instability of policy optimization, potentially improving real-world robustness and OOD generalization without sacrificing VLM capabilities. The public release of code and data is a clear strength for reproducibility.

major comments (1)
  1. [Abstract] Abstract: the central claim that SyVLA achieves 'superior task success rates and stronger out-of-distribution generalization' is stated without any experimental details, baselines, metrics, error analysis, or quantitative results. This absence makes it impossible to evaluate whether the data support the performance claims that constitute the paper's main contribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the need for more concrete details in the abstract. We address this point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that SyVLA achieves 'superior task success rates and stronger out-of-distribution generalization' is stated without any experimental details, baselines, metrics, error analysis, or quantitative results. This absence makes it impossible to evaluate whether the data support the performance claims that constitute the paper's main contribution.

    Authors: We agree that the abstract would be strengthened by including key quantitative results. The full paper reports these details in Sections 4 and 5 (e.g., task success rates on real-world robotic tasks, OOD generalization metrics on multi-modal benchmarks, comparisons to baselines such as RT-2 and OpenVLA, and ablation studies). In the revised version we will add concise numerical highlights to the abstract, such as average success rate improvements and specific OOD metrics, while preserving the word limit. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The provided abstract and context describe two algorithmic contributions (Intention Decoupling and similar-sample guided RL) whose value is asserted via empirical results on robotic tasks and benchmarks. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the text. The central claims rest on experimental outcomes rather than any reduction to inputs by construction. With no load-bearing mathematical steps present, the derivation chain (such as it is) is self-contained and non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract mentions no free parameters, background axioms, or new postulated entities.

pith-pipeline@v0.9.1-grok · 5660 in / 996 out tokens · 22121 ms · 2026-06-27T17:11:20.670356+00:00 · methodology

discussion (0)

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Reference graph

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    12 Scaling by Diversified Experience for Vision-Language-Action Models A. More Implementation Details We describe the implementation details of our SyVLA model. We choose Qwen2.5-VL 3B as the VLM of SyVLA. For the Feature Query Tokens, we use a fixed set of learnable tensors. Across all three experiments, we use 20 Feature Query Tokens with a dimensionali...

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    This corresponds to what we describe in Section 3.1 and 3.2, i.e., C= Adapter(H)

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    Task 2 Task 3 w/o Similar Retrival 0.56 0.571 SyVLA 0.68 0.643 Table 10.Ablation study of RL algorithm on the other 2 Tasks. We also compare ourSimilar-Sample Guidance RLalgorithm with recent SOTA RL method SimpleVLA-RL (Li et al., 2025a) and Pi-RL (Chen et al., 2025a) onFold Shirttask. The results are shown in Table 11 Pi-RL Simple VLA SyVLA 0.714 0.357 ...

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    synthesize actions through multi-step denoising, enabling finer-grained control and dexterous manipulation; however, they commonly use pretrained VLMs only as initialization, leading to catastrophic forgetting of general vision–language capabilities after VLA training. Recent works (Zhou et al., 2025a;b; Zhai et al., 2025; Intelligence et al., 2025b) mix ...

    2025

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    to represent actions at intermediate layers and adopt a two-stage generation paradigm; such methods are typically sensitive to the scale and alignment quality of pretraining data, and may produce meaningless action token generations when data are insufficient. Our approach leverages Feature Query Token and the Intention Decoupling algorithm to effectively...

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    improves capability through a two-stage pipeline—offline reinforcement learning followed by online reinforcement learning—but has only been validated on small diffusion models, and its effectiveness for VLA-scale models remains to be established. Some other methods also attempt to improve the data efficiency and safety through residual policies (Xiao et a...

    2024