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3D HAMSTER achieves superior robotic manipulation performance by directly predicting 3D trajectories, eliminating the distortions caused by 2D guidance.
Zero-shot transfer of a refined RL policy boosts manipulation success rates from 42% to 76% on real robots, showcasing a breakthrough in sim-to-real applications.
Membrane achieves the highest safety performance against evolving jailbreaks while keeping benign refusals remarkably low, revolutionizing LLM defense strategies.
Off-policy RL can now train in minutes instead of hours for sim-to-real robot control, thanks to a new algorithm that borrows scaling laws from supervised learning.
Unlock the potential of full-duplex speech language models with Sommelier, a new open-source pipeline that tackles the messy reality of multi-speaker conversations.
Injecting noise estimates layer-by-layer into diffusion models dramatically improves speech enhancement in complex, real-world conditions where single-point injection fails.
