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Multi-axis max@K boosts image diversity and fairness in text-to-image generation, enhancing representation without sacrificing quality.
Centering advantages in policy gradients can drastically reduce variance and improve performance in reinforcement learning tasks.
OrderGrad transforms policy-gradient optimization by enabling precise control over distributional properties, allowing for risk-averse and exploratory learning in real-world applications.
Discontinuous dynamics aren't the biggest problem for policy gradients in differentiable simulators; variance control often matters more.
