Intrinsic Task Symmetry Drives Generalization in Algorithmic Tasks

Researchers propose that intrinsic task symmetries drive 'grokking' - the sudden transition from memorization to generalization in neural networks. The study identifies a three-stage training process and introduces diagnostic tools to predict and accelerate the onset of generalization in algorithmic reasoning tasks.

• →Grokking follows a consistent three-stage pattern: memorization, symmetry acquisition, and geometric organization.
• →Intrinsic task symmetries are identified as the primary driver enabling neural networks to move beyond memorization.
• →Generalization emerges during the symmetry acquisition phase, before representations reorganize into structured geometry.
• →The research validates this symmetry-driven approach across diverse algorithmic domains including algebraic and relational reasoning.
• →A symmetry-based diagnostic tool is introduced to predict generalization onset and propose acceleration strategies.