Go to COLM 2024 Conference homepageUnified View of Grokking, Double Descent and Emergent Abilities: A Comprehensive Study on Algorithm Task
Recent studies have uncovered intriguing phenomena in deep learning, such as grokking, double descent, and emergent abilities in large language models, which challenge human intuition and are crucial for a deeper understanding of neural models. In this paper, we present a comprehensive study on algorithm task to provide a unified view of these three phenomena, with a focus on the interplay between memorization and generalization. Through extensive experiments spanning a wide range of model sizes and training data quantities, we uncover four distinct training dynamics, each arising from unique combinations of model size and training data quantity, formulating a theoretical framework for further analysis. Utilizing this framework, we establish connections between double descent and grokking and propose two verifiable predictions regarding the occurrence of double descent, both substantiated by our experimental results. Moreover, we expand our experiments to the multi-task learning paradigm, demonstrating how algorithm tasks can be turned into emergent abilities by mixing some pure memorization data. This offers a novel perspective to understand emergent abilities in Large Language Models.