景观感知增长：微型LAG的力量

最近，人们对高效预训练范式以训练基于Transformer模型的大型模型方面越来越感兴趣。 Several recent approaches use smaller models to initialize larger models in order to save computation (e.g., stacking and fusion)。 In this work, we study the fundamental question of how to select the best growing strategy from a given pool of growing strategies. Prior works have extensively focused on loss- and/or function-preserving behavior at initialization or simply performance at the end of training. Instead, we identify that behavior at initialization can be misleading as a predictor of final performance and present an alternative perspective based on early training dynamics, which we call "landscape-aware growing (LAG)"。 We perform extensive analysis of correlation of the final performance with performance in the initial steps of training and find early and more accurate predictions of the optimal growing strategy (i.e., with only a small "lag" after initialization)。 This perspective also motivates an adaptive strategy for gradual stacking.