AGENTIC-SYSTEMSApril 28, 20261 min read8 views

The Power of Power Law: Asymmetry Enables Compositional Reasoning

New research reveals that training AI on power-law distributions consistently outperforms uniform distributions for compositional reasoning tasks. This asymmetry allows models to acquire high-frequency skills first, which then act as stepping stones to efficiently learn rare, long-tailed skills.

Computer Science > Artificial Intelligence

Title:The Power of Power Law: Asymmetry Enables Compositional Reasoning

View PDF HTML (experimental)Abstract:Natural language data follows a power-law distribution, with most knowledge and skills appearing at very low frequency. While a common intuition suggests that reweighting or curating data towards a uniform distribution may help models better learn these long-tail skills, we find a counterintuitive result: across a wide range of compositional reasoning tasks, such as state tracking and multi-step arithmetic, training under power-law distributions consistently outperforms training under uniform distributions. To understand this advantage, we introduce a minimalist skill-composition task and show that learning under a power-law distribution provably requires significantly less training data. Our theoretical analysis reveals that power law sampling induces a beneficial asymmetry that improves the pathological loss landscape, which enables models to first acquire high-frequency skill compositions with low data complexity, which in turn serves as a stepping stone to efficiently learn rare long-tailed skills. Our results offer an alternative perspective on what constitutes an effective data distribution for training models.

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Source: arXiv cs.AI Recent

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