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Single-step action generation can outperform multi-step diffusion methods in offline reinforcement learning, achieving higher performance with lower computational costs.
Diffusion Transformers waste up to 66% of their conditional embedding space without sacrificing generation quality, hinting at opportunities for more efficient conditioning.
Unlock the potential of your offline RL data: a new framework achieves state-of-the-art performance on D4RL benchmarks by quantifying and leveraging data uncertainty with a computationally efficient Rank-One MIMO architecture.
