Trajectory self-distillation trains few-step diffusion language models to match full-step trajectories, mitigating factorization error to enable fast, high-quality parallel decoding.
Residual Quantization maps contexts to discrete additive codes enabling nonlinear contextual bandits with strictly bounded memory, beating linear variants on 11 of 13 datasets and matching heavy retrained baselines with up to 1000x less memory.
PISCO enables precise video instance insertion via sparse keyframe control while preserving dynamics, achieving monotonic gains with added signals and outperforming editing baselines.