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The Hadamard-CLIP architecture restores universal approximation capabilities for multimodal contrastive learning, overcoming significant limitations of existing models.
Finally, a reinforcement learning algorithm, PGP, can provably find near-optimal policies that respect safety and resource constraints, even when the policy space is non-convex.
