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Models trained with ACA-RL not only outperform on missing-premise tasks but also redefine how we evaluate reasoning under uncertainty in NLP.
Achieve diffusion-level perceptual quality in monocular depth estimation at 40x the speed, by replacing the slow initial diffusion steps with a fast ViT-based depth map and refining in a compact latent space.
Achieve faithful textile pattern generation by disentangling clothing features and guiding a diffusion model with fine-grained alignment, outperforming existing image-to-image methods.