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RL agents can learn far more efficiently by incorporating group-level natural language feedback, achieving 2.2x sample efficiency gains in sparse-reward environments.
Anisotropic noise schedules, optimized via a novel variational framework, can significantly boost diffusion model performance across various datasets and NFE regimes.
