A new research paper explores the optimization challenges of Transformer models, particularly in fine-tuning scenarios. The study identifies gradient heterogeneity, the variation in gradient norms across parameter blocks, as a key factor that, along with Hessian heterogeneity, hinders the convergence of standard gradient-based methods like SGD. The research suggests that adaptive optimizers such as Adam and sign-based methods like SignSGD are less sensitive to this heterogeneity. The paper also points to the placement of layer normalization, with Post-LN architectures showing particularly pronounced heterogeneity, as a significant influence on this issue. Experimental validation was conducted on Transformers used in both natural language processing and vision tasks. AI
IMPACT Identifies a key factor in Transformer optimization, potentially guiding future improvements in training efficiency and model performance.
RANK_REASON The cluster contains a research paper detailing theoretical analysis and experimental validation of optimization techniques for Transformer models. [lever_c_demoted from research: ic=1 ai=1.0]
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