Weak-to-Strong Generalization Through the Data-Centric Lens

TL;DR

This paper introduces the concept of overlap density as a data-centric measure to understand and improve weak-to-strong generalization in machine learning, supported by theoretical analysis and empirical validation.

Contribution

It proposes a simple, practical overlap detection algorithm and demonstrates how maximizing overlap density enhances weak-to-strong generalization.

Findings

Overlap density correlates with generalization performance.

The proposed algorithm effectively identifies key data points for learning.

Empirical results validate the theoretical benefits of maximizing overlap density.

Abstract

The weak-to-strong generalization phenomenon is the driver for important machine learning applications including highly data-efficient learning and, most recently, performing superalignment. While decades of research have resulted in numerous algorithms that produce strong empirical performance, understanding what aspects of data enable weak-to-strong generalization has been understudied. We propose a simple data-centric mechanism that characterizes weak-to-strong generalization: the overlap density. Intuitively, generalization tracks the number of points that contain overlaps, i.e., both easy patterns (learnable by a weak model) and challenging patterns (only learnable by a stronger model), as with such points, weak predictions can be used to learn challenging patterns by stronger models. We provide a practical overlap detection algorithm to find such points in datasets and leverage them to learn, among multiple sources of data, which to query when seeking to maximize overlap density and thereby enhance weak-to-strong generalization. We present a theoretical result showing that the generalization benefit is a function of the overlap density and a regret bound for our data selection algorithm. Empirically, we validate the mechanism and the overlap detection algorithm on a wide array of settings.