Recursive KL Divergence Optimization: A Dynamic Framework for Representation Learning
This provides a more efficient optimization landscape for representation learning, particularly beneficial for resource-constrained applications.
The authors tackled the problem of inefficient representation learning by proposing Recursive KL Divergence Optimization (RKDO), a dynamic framework that reframes learning as recursive divergence alignment, which achieved approximately 30% lower loss values and 60-80% reduction in computational resources compared to static approaches.
We propose a generalization of modern representation learning objectives by reframing them as recursive divergence alignment processes over localized conditional distributions While recent frameworks like Information Contrastive Learning I-Con unify multiple learning paradigms through KL divergence between fixed neighborhood conditionals we argue this view underplays a crucial recursive structure inherent in the learning process. We introduce Recursive KL Divergence Optimization RKDO a dynamic formalism where representation learning is framed as the evolution of KL divergences across data neighborhoods. This formulation captures contrastive clustering and dimensionality reduction methods as static slices while offering a new path to model stability and local adaptation. Our experiments demonstrate that RKDO offers dual efficiency advantages approximately 30 percent lower loss values compared to static approaches across three different datasets and 60 to 80 percent reduction in computational resources needed to achieve comparable results. This suggests that RKDOs recursive updating mechanism provides a fundamentally more efficient optimization landscape for representation learning with significant implications for resource constrained applications.