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Compact diffusion models can now leverage the power of high-capacity Teachers without architectural changes, achieving remarkable performance gains.
Optimizing threading in RTAC cuts inference latency and boosts control responsiveness, making robotic manipulation viable for low-cost agricultural applications.
Weak-to-strong reward models can ace the test but still fail in the real world, revealing a hidden brittleness in current preference learning approaches.
Forget waiting a minute for garment generation: SwiftTailor slashes inference times while boosting accuracy by representing 3D garments as geometry images.
