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Distribution-wise rewards can drastically enhance image diversity and quality in generative models, reducing mode collapse and reward hacking issues.
Forget expensive data curation: a simple, training-free entropy metric lets you train LLMs on just 20% of your reasoning data without sacrificing performance.
Item agents that self-promote can simultaneously boost recommendation accuracy and fairness, overturning the assumption that these goals are inherently at odds.
