Kavli Affiliate: Zheng Zhu
| First 5 Authors: Angen Ye, Angen Ye, , ,
| Summary:
Vision-Language-Action (VLA) models aim to unify perception, language
understanding, and action generation, offering strong cross-task and
cross-scene generalization with broad impact on embodied AI. However, current
VLA models often lack explicit step-by-step reasoning, instead emitting final
actions without considering affordance constraints or geometric relations.
Their post-training pipelines also rarely reinforce reasoning quality, relying
primarily on supervised fine-tuning with weak reward design. To address these
challenges, we present VLA-R1, a reasoning-enhanced VLA that integrates
Reinforcement Learning from Verifiable Rewards (RLVR) with Group Relative
Policy Optimization (GRPO) to systematically optimize both reasoning and
execution. Specifically, we design an RLVR-based post-training strategy with
verifiable rewards for region alignment, trajectory consistency, and output
formatting, thereby strengthening reasoning robustness and execution accuracy.
Moreover, we develop VLA-CoT-13K, a high-quality dataset that provides
chain-of-thought supervision explicitly aligned with affordance and trajectory
annotations. Furthermore, extensive evaluations on in-domain, out-of-domain,
simulation, and real-robot platforms demonstrate that VLA-R1 achieves superior
generalization and real-world performance compared to prior VLA methods. We
plan to release the model, code, and dataset following the publication of this
work. Code: https://github.com/GigaAI-research/VLA-R1. Website:
https://gigaai-research.github.io/VLA-R1.
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