Scaling by Diversified Experience for Vision-Language-Action Models

• URL: http://arxiv.org/abs/2606.09009v1
• Date: Mon, 08 Jun 2026 04:13:38 GMT
• Title: Scaling by Diversified Experience for Vision-Language-Action Models
• Abstract summary: We introduce SyVLA, a robust VLA model trained with diversified experiences.<n>We propose an Intention Decoupling algorithm to isolate control-relevant features from reasoning contexts.<n>Experiments on real-world robotic tasks and multi-modal benchmarks demonstrate that SyVLA achieves superior task success rates.
• Score: 42.05618635021853
• License: http://creativecommons.org/licenses/by/4.0/
• 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}.

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