VLA-R1: Enhancing Reasoning in Vision-Language-Action Models

Vision-Language-Action (VLA) models aim to unify per-ception, language understanding, and action generation, offering strongcross-task and cross-scene generalization with broad impact on embodiedAI. However, current VLA models often lack explicit step-by-step rea-soning, instead emitting final actions without considering affordance con-straints or geometric relations. Their post-training pipelines also rarelyreinforce reasoning quality, relying primarily on supervised fine-tuningwith weak reward design. To address these challenges, we present VLA-R1, a reasoning-enhanced VLA that integrates Reinforcement Learningfrom Verifiable Rewards (RLVR) with Group Relative Policy Optimiza-tion (GRPO) to systematically optimize both reasoning and execution.Specifically, we design an RLVR-based post-training strategy with ver-ifiable rewards for region alignment, trajectory consistency, and outputformatting, thereby strengthening reasoning robustness and executionaccuracy. Moreover, we develop VLA-CoT-13K, a high-quality datasetthat provides chain-of-thought supervision explicitly aligned with affor-dance and trajectory annotations. Furthermore, extensive evaluations onin-domain, out-of-domain, simulation, and real-robot platforms demon-strate that VLA-R1 achieves superior generalization and real-world per-formance compared to prior VLA methods. We plan to release the model,code, and dataset following the publication of this work.