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Distribution-wise rewards can drastically enhance image diversity and quality in generative models, reducing mode collapse and reward hacking issues.
RootMem transforms how personalized LLMs access critical logical memories, outperforming traditional methods by integrating structured decision logic into memory retrieval.
LLMs show significant variability in the actionability of their UX critiques, with some models outperforming others across different product categories.
A 5B model just crushed the image generation and editing performance of models 5-16x larger, thanks to smarter feature fusion and a novel RL training strategy.
