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MDD achieves up to 2.95x faster video generation while preserving visual fidelity by decoupling magnitude and directional components in the denoising process.
AdaThinkV achieves 40.79% accuracy in video reasoning while using 22.7% fewer tokens than its strongest adaptive baseline, showcasing a breakthrough in token-efficient reasoning.
Bounded reward noise can lead to a dramatic reduction in regret bounds, achieving \(O(\log T)\) versus the standard \(\tilde{O}(\sqrt{T})\) for sub-Gaussian conditions.
