Researchers have explored a method for training Transformer models by incrementally adding new layers to a frozen base, maintaining a constant budget for trainable parameters. This approach, termed 'Growing Transformers,' demonstrated that new blocks could be trained effectively while only updating a small fraction of the model's parameters. Even with a highly constrained token interface, a 16-layer model achieved a notable MMLU score, suggesting viability for continued learning under parameter budget limitations, albeit with a trade-off in final perplexity compared to monolithic training. AI
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IMPACT This research suggests a potential pathway for more parameter-efficient model scaling and continued learning.
RANK_REASON The cluster contains an arXiv paper detailing a novel training methodology for Transformer models. [lever_c_demoted from research: ic=1 ai=1.0]