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Shanghai Innovation Institute, University of Science and Technology
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Stochastic modality masking during training can boost bimanual robotic manipulation success rates by over 30% without complex architectural changes.
Surprisingly, the "think before answer" paradigm fails to enhance generative recommendation models, prompting a novel approach that redefines how reasoning is integrated into these systems.
Scheduling when learning signals are applied can dramatically enhance the stability and efficiency of policy evolution in RLVR.
Sub-linear attention is now possible without sacrificing complete long-range dependency retention, thanks to learnable summary tokens that compress context.
