Warmstarting for Scaling Language Models
This work addresses the cost and tuning challenges in scaling language models, offering an incremental improvement for researchers and practitioners in AI.
The paper tackles the problem of reducing the high training costs of large language models by exploring warmstarting from smaller, cheaper-to-tune models, finding that techniques like weight shrinking, zero-padding, and μP-based initialization enable effective warmstarting with preserved training dynamics.
Scaling model sizes to scale performance has worked remarkably well for the current large language models paradigm. The research and empirical findings of various scaling studies led to novel scaling results and laws that guides subsequent research. High training costs for contemporary scales of data and models result in a lack of thorough understanding of how to tune and arrive at such training setups. One direction to ameliorate the cost of pretraining large models is to warmstart the large-scale training from smaller models that are cheaper to tune. In this work, we attempt to understand if the behavior of optimal hyperparameters can be retained under warmstarting for scaling. We explore simple operations that allow the application of theoretically motivated methods of zero-shot transfer of optimal hyperparameters using μTransfer. We investigate the aspects that contribute to the speedup in convergence and the preservation of stable training dynamics under warmstarting with μTransfer. We find that shrinking smaller model weights, zero-padding, and perturbing the resulting larger model with scaled initialization from μP enables effective warmstarting of $\mut{}$.