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We’ve developed a new approach to training models, Harmonic Reasoning, which creates a "harmonic" interplay between asynchronous, continuous-time streams of sensing and acting tokens. ⚙️🎵 Watch GEN-0 pack a camera. 🤖📸

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Like LLMs, autoregressive transformers for action tokens need a reasoning layer to reduce hallucinations and boost reliability. Grounding this layer in the physics of the action space using DVBFs makes for scalable, task-agnostic training—far simpler than creating RL reward functions for each task. Learn more about our novel approach in the video below 👇🏼

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