VLA-R1: Enhancing Reasoning in Vision-Language-Action Models
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.
68df33ebdf49fb0df1e03c92
Angen Ye
68df33ebdf49fb0df1e03c93
Zeyu Zhang
claimed_verified
2025-10-05T12:47:08.590Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
user
SteveZeyuZhang
68df33ebdf49fb0df1e03c94
Boyuan Wang
68df33ebdf49fb0df1e03c95
Xiaofeng Wang
claimed_verified
2025-10-23T15:02:42.307Z
6426616ea5ec4a5cbc535634
https://cdn-avatars.huggingface.co/v1/production/uploads/6426616ea5ec4a5cbc535634/hH6JsxnXeakH3mBTeNNmO.jpeg
JeffWang
user
Jeff-Wang
68df33ebdf49fb0df1e03c96
Dapeng Zhang
68df33ebdf49fb0df1e03c97
Zheng Zhu
VLA-R1: Enhancing Reasoning in Vision-Language-Action Models
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.
VLA-R1 enhances VLA models with RLVR and GRPO to improve reasoning and execution, achieving better generalization and real-world performance using a new dataset with chain-of-thought supervision.
VLA models Reinforcement Learning from Verifiable Rewards (RLVR) Group Relative Policy Optimization (GRPO) region alignment trajectory consistency output formatting chain-of-thought supervision affordance trajectory annotations