Adaptive Layers: Unleashing Transform Potential Through Dynamic Learning Rate Optimization

The Transformer model has achieved remarkable success in tasks such as natural language processing; however, its training efficiency poses challenges when handling large-scale data and models. Traditional learning rate scheduling methods often apply a uniform learning rate across all layers, overlooking the distinct characteristics of each layer, which results in slower training and suboptimal convergence. To address this issue, we propose a novel Layerwise Learning Rate Adaptation (LLRA) method. This approach dynamically adjusts the learning rates across layers by integrating cosine decay and layer-specific warmup periods. Through experiments on multiple character-level language modeling tasks such as Shakespeare_char, enwik8, and text8, LLRA significantly reduces training time while maintaining or improving model performance. Our research offers new insights into enhancing the training efficiency of Transformer models and demonstrates its broad potential for various applications. Future work will explore the application of LLRA to larger models and other tasks.