<ul data-eligibleForWebStory="true"><li>A new energy-based transformer model architecture enhances AI systems' reasoning capabilities and robustness.</li><li>The model proposes 'thinking as optimization' approach, using a learned verifier for candidate predictions.</li><li>Unlike existing approaches, this model effectively combines generators and verifiers for better generalization.</li><li>The model outperforms existing architectures in efficiency during pretraining and inference for reasoning tasks.</li><li>Researchers believe the model's scalability and generalization benefits make it promising for AI applications.</li></ul>

A new paradigm for AI: How ‘thinking as optimization’ leads to better general-purpose models

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