OmniVLA-RL: A Vision-Language-Action Model with Spatial Understanding and Online RL
On the map: Vision-language-action models → Reinforcement learning and post-training
Abstract (arXiv)
Visual-Language-Action (VLA) models represent a paradigm shift in embodied AI, yet existing frameworks often struggle with imprecise spatial perception, suboptimal multimodal fusion, and instability in reinforcement learning. To bridge these gaps, we propose OmniVLA-RL, a novel architecture that leverages a Mix-of-Transformers (MoT) design to synergistically integrate reasoning, spatial, and action experts. Furthermore, we introduce Flow-GSPO, which reformulates flow matching as a Stochastic Differential Equation (SDE) process and integrates it with Group Segmented Policy Optimization (GSPO) to enhance action precision and training robustness. Extensive evaluations on the LIBERO and LIBERO-Plus benchmarks demonstrate that OmniVLA-RL achieves decent overall performance and surpasses mainstream existing methods, effectively overcoming the fundamental limitations of current VLA models.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning and post-training, one of 13 directions in Vision-language-action models. Builds on 49 of its 54 references that are in the map. First posted on arXiv 20 Apr 2026.
Builds on
49 of 54 references.
Closest ancestors · 2
Same direction, newest first
- 2025π*0.6: a VLA That Learns From Experience18 Nov
- 2025VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning24 May
Other references · 47
- 2017Attention Is All You Needcited by 60,361
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- 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,109
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 3,080
35 more
- 2024OpenVLA: An Open-Source Vision-Language-Action Modelcited by 2,887
- 2015Trust Region Policy Optimizationcited by 2,580
- 2024π0: A Vision-Language-Action Flow Model for General Robot Controlcited by 2,407
- 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,224
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- 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilitiescited by 706
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- 2025SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Modelcited by 519
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- 2025Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Modelcited by 134
- 2025DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Drivingcited by 134
- 2024FlowPolicy: Enabling Fast and Robust 3D Flow-based Policy via Consistency Flow Matching for Robot Manipulationcited by 104
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- 2026StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
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- 2026Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models