Jinghao Zhang

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.