OmniVLA-RL: A Vision-Language-Action Model with Spatial Understanding and Online RL

• URL: http://arxiv.org/abs/2604.17706v2
• Date: Fri, 24 Apr 2026 09:42:43 GMT
• Title: OmniVLA-RL: A Vision-Language-Action Model with Spatial Understanding and Online RL
• Abstract summary: Visual-Language-Action (VLA) models represent a paradigm shift in embodied AI.<n>Existing frameworks struggle with imprecise perception, suboptimal multimodal fusion, and instability in reinforcement learning.<n>We propose OmniVLA-RL, a novel architecture that leverages a Mix-of-Transformers (MoT) design to integrate reasoning, spatial, and action experts.
• Score: 1.880672844596704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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