Current Vision-Language-Action (VLA) models rely on fixed computational depth, expending the same amount of compute on simple adjustments and complex multi-step manipulation. While Chain-of-Thought (CoT) prompting enables variable computation, it scales memory linearly and is ill-suited for continuous action spaces. We introduce Recurrent-Depth VLA (RD-VLA), an architecture that achieves computational adaptivity via latent iterative refinement rather than explicit token generation. RD-VLA employs a recurrent, weight-tied action head that supports arbitrary inference depth with a constant memory footprint. The model is trained using truncated backpropagation through time (TBPTT) to efficiently supervise the refinement process. At inference, RD-VLA dynamically allocates compute using an adaptive stopping criterion based on latent convergence. Experiments on challenging manipulation tasks show that recurrent depth is critical: tasks that fail entirely (0 percent success) with single-iteration inference exceed 90 percent success with four iterations, while simpler tasks saturate rapidly. RD-VLA provides a scalable path to test-time compute in robotics, replacing token-based reasoning with latent reasoning to achieve constant memory usage and up to 80x inference speedup over prior reasoning-based VLA models. Project page: https://rd-vla.github.io/

翻译：当前视觉-语言-动作模型依赖于固定的计算深度，在简单调整和复杂多步操作上消耗相同的计算量。虽然思维链提示支持可变计算，但其内存需求呈线性增长，且不适用于连续动作空间。我们提出循环深度视觉-语言-动作模型，该架构通过潜在迭代优化而非显式标记生成实现计算自适应性。该模型采用循环权重共享的动作头，在恒定内存占用下支持任意推理深度。通过时间截断反向传播训练模型，以有效监督优化过程。在推理阶段，该模型基于潜在收敛的自适应停止准则动态分配计算。在复杂操作任务上的实验表明循环深度至关重要：单次迭代推理完全失败的任务在四次迭代后成功率超过90%，而简单任务则快速达到饱和。该模型为机器人领域的测试时计算提供了可扩展路径，用潜在推理替代基于标记的推理，实现恒定内存占用，相比先前基于推理的视觉-语言-动作模型获得高达80倍的推理加速。项目页面：https://rd-vla.github.io/