Excited to see S1 finally go public! 🚀 This release also marks exactly one year since I transitioned from vision research to robotics. If I had to share the single biggest lesson from that journey, it’s this: Before asking "How do we train a better model?", we need to ask "How should the model behave at inference time?" In academia, we tend to attack problems the same way: set up a benchmark, tweak the architecture or training recipe, and watch the score climb. Today, much of robotics research runs this similar loop: pit VLAs against WAMs, introduce a new loss function, test a different action representation. Model performance on benchmarks keeps improving, but inference-time behavior stays simplistic and fixed. But my past year at @SkildAI taught me that raw model performance isn't the ultimate driver of real-world success. Inference-time behavior is. The real world guarantees high stochasticity and endless out-of-distribution scenarios. The dominant deployment paradigm (taking the past X seconds of observations to predict the next Y seconds of actions) won't get us to general-purpose robotics, because every robotic action has compounding consequences. A vision model can guess wrong on a static test set with zero impact on the next sample. A robot cannot. It operates in a closed loop: its own mistakes create the very distribution shifts it must then survive. "Collect more data, fine-tune, redeploy" is not a scalable answer. To truly generalize, a model must adapt on the fly. So the order of operations has to invert. First, define an inference-time behavior scalable and robust enough to handle the real world. Then work backward to shape data collection and model training. Everything in the S1 release stems from this inversion. Language modeling already taught us this lesson: LLMs didn't crack complex multi-step math just because someone threw more tokens at pre-training. Instead, the breakthrough came from changing the inference-time behavior first (reasoning step by step) and then redesigning training around it. Betting that data scale alone will magically produce emergent physical skills is just as naive. Robotics needs the same shift. S1 is proof that working backward from real-world inference changes everything. And this is just the starting point. Check out the release below, and enjoy the rest of your day! 👇