Recent advances in Vision-Language-Action models (VLAs) have expanded the capabilities of embodied intelligence. However, significant challenges remain in real-time decision-making in complex 3D environments, which demand second-level responses, high-resolution perception, and tactical reasoning under dynamic conditions. To advance the field, we introduce CombatVLA, an efficient VLA model optimized for combat tasks in 3D action role-playing games(ARPGs). Specifically, our CombatVLA is a 3B model trained on video-action pairs collected by an action tracker, where the data is formatted as action-of-thought (AoT) sequences. Thereafter, CombatVLA seamlessly integrates into an action execution framework, allowing efficient inference through our truncated AoT strategy. Experimental results demonstrate that CombatVLA not only outperforms all existing models on the combat understanding benchmark but also achieves a 50-fold acceleration in game combat. Moreover, it has a higher task success rate than human players. We will open-source all resources, including the action tracker, dataset, benchmark, model weights, training code, and the implementation of the framework at https://combatvla.github.io/.

本研究针对当前视觉-语言-行动模型在复杂3D环境中实时决策的不足，提出了一种专为3D动作角色扮演游戏战斗任务优化的高效模型CombatVLA。研究表明，CombatVLA在战斗理解基准上超越了现有所有模型，并在游戏战斗中实现了50倍的加速，同时任务成功率高于人类玩家。

CombatVLA：用于3D动作角色扮演游戏中战斗任务的高效视觉-语言-行动模型