Introducing TacitVLA — the benefits of reasoning, without the latency! There is now conclusive evidence that #reasoning# supervision can improve the performance of Vision-Language-Action (VLA) models. More broadly, reasoning is a promising tool for building more capable and safer autonomy stacks. But generating a chain of thought token by token adds latency—a major bottleneck for real-time robotics and autonomous driving. TacitVLA introduces a training approach that captures the benefits of reasoning without requiring actions to wait for it. The key insight: reasoning traces improve the model’s backbone representations during training, allowing actions to benefit from this supervision without waiting for or conditioning on generated reasoning at inference time. For long-horizon tasks, reasoning traces can serve as episodic memory, generated asynchronously during action execution without adding latency when generation finishes within the execution window. 📈 Across four manipulation and autonomous-driving benchmarks, TacitVLA matches the performance of its explicit-reasoning counterpart at up to 3.4× lower latency and achieves up to 17.1 percentage points higher success than the same architecture trained without reasoning, at comparable latency. 🌐 Project page: 📄 Paper: This work was led by @Jacklu_me, in collaboration with @YurongYou @Boyiliee @yulong_c @tianran_ @ChaoweiX Xiangyu Chen, Wenjie Luo, @iamborisi @yan_wang_9 . @NVIDIAAI @NVIDIADRIVE @NVIDIARobotics #Robotics# #PhysicalAI# #EmbodiedAI# #Reasoning#