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Adaptive imagination can transform limited target data into reliable rollouts, enabling robust sim-to-real transfer in visual reinforcement learning.
Selective layer looping in masked diffusion language models unlocks surprising gains in both training efficiency and reasoning performance, beating naive depth scaling.
