The Breakthrough AI Scaling Desperately Needed

When Transformers were introduced, the entire AI ecosystem underwent a reform. But there was a problem. When a model was large enough, and researchers wanted to train a specific part of it, the only option was to retrain the entire model from scratch.  This was a critical issue. To address it, researchers from Google, Max Planck Institute, and Peking University introduced a new approach called TokenFormer.  The innovation lies in treating model parameters as tokens themselves, allowing for a dynamic interaction between input tokens and model parameters through an attention mechanism rather than fixed linear projections. The traditional Transformer architecture faces a significant challenge when scaling—it requires complete retraining from scratch when architectural mod