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Small models trained from scratch are outperforming standard LLMs: here's how

Small models trained from scratch are quietly outperforming standard LLMs on targeted tasks, and the architecture behind them deserves attention. The Universal Transformer replaces stacked layers with recurrent loops that refine token representations through shared computation, a simpler approach that scales with depth rather than parameter count. What's striking is that these models don't need internet-scale pre-training to win. The real question is whether frontier labs have already folded these enhancements into production systems, or if this research remains ahead of its time.