According to DeepLearning.AI, researchers introduced the Energy-Based Transformer (EBT), which evaluates candidate next tokens by assigning an 'energy' score and then iteratively reduces this energy through gradient steps to verify and select the optimal token. In empirical trials using a 44-million-parameter model on the RedPajama-Data-v2 dataset, the EBT architecture surpassed same-size vanilla transformers on three out of four key AI benchmarks. This approach demonstrates a practical advancement in generative transformer models, suggesting new opportunities for improving language model efficiency and accuracy in business applications such as conversational AI and large-scale document processing (source: DeepLearning.AI, Sep 27, 2025).