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Integrating item-based collaborative filtering into discrete diffusion models leads to a substantial boost in recommendation accuracy, outperforming existing methods.
BehaviorWorldGen transforms how we generate realistic multi-agent interactions, leading to substantial improvements in action model performance.
Stochastic modality masking during training can boost bimanual robotic manipulation success rates by over 30% without complex architectural changes.
Forget external teachers – the best way to boost your RL model's performance is to learn from its future self.
RL fine-tuning of discrete diffusion models can be made dramatically more stable and effective by treating the final denoised sample as the action and reconstructing trajectories using the forward diffusion process.
