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Flow-based imitation learning can be significantly improved by distilling both rewards and actions on-policy, enabling more robust and generalizable policies, especially with limited or noisy demonstrations.
Distilling black-box video generators into autoregressive models doesn't require teacher scores or complex alignment鈥攋ust cleverly paired rollouts and a discriminator.
Current audio-visual models nail unimodal quality but still struggle to make music and dance move together rhythmically, highlighting a key gap TMD-Bench is designed to address.
