Note: This post summarizes my capstone project for the AI Alignment course by BlueDot Impact. You can learn more about their amazing courses here and consider applying!

Introduction

Recent research in weak-to-strong generalization (W2SG) has revealed a crucial insight: enhancing weak supervisors to train strong models relies more on the characteristics of the training data rather than on new algorithms. This article reviews the research conducted by Shin et al. (2024), who identified overlap density — a measurable data attribute that can predict and support successful W2SG. Their findings suggest we've been looking at the alignment problem through the wrong lens — instead of only focusing on model architectures, we should also be engineering datasets that maximize this critical density property. By analyzing their work and implementing their algorithms, I aim to provide researchers with tools to further investigate data-centric features that improve W2SG.

To apply the information from the article practically, you can use my research toolkit, which implements the overlap density algorithms mentioned in the research.

Background: W2SG's Data-Centric Foundation

Weak-to-Strong Context:

In the AI alignment paradigm first proposed by Burns et al. (2023), W2SG enables a weak model (e.g., GPT-2) to train a significantly stronger model (e.g., GPT-4) through carefully structured interactions. W2SG describes the transition from weak generalization, where a model performs well on “easy” patterns (i.e. patterns with clear, simple features or high-frequency occurrences in the training data), to strong generalization, where the model successfully handles “hard” patterns (low-frequency, high-complexity features). This becomes crucial when:

Human oversight can't scale with AI capabilities
We need to bootstrap supervision for superintelligent systems
Developing failsafes against mesa-optimizers^[1]

Current ML models often excel at weak generalization, but their capacity for strong generalization remains inconsistent and poorly understood. This gap has major implications for AI alignment: systems that generalize weakly may fail in unanticipated ways under novel conditions, leading to dangerous behaviors.

Key Definitions:

Figure 1: Concept behind Overlap Density and how it influences W2SG^[2]

Shin et al. formalize easy patterns as features that are learnable by both weak and strong model...

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