Limitations of refinement methods for weak to strong generalization

TL;DR

This paper critically examines the limitations of label refinement and weak training methods for aligning large language models, revealing inherent irreducible errors and performance gaps compared to ideal oracle approaches.

Contribution

It provides a theoretical analysis showing the fundamental limitations of current refinement techniques and suggests the need for new methods to achieve better generalization.

Findings

Both weak training and label refinement have irreducible error barriers.

There is a performance gap between practical methods and oracle approaches.

Results motivate exploring alternative strategies for better alignment.

Abstract

Standard techniques for aligning large language models (LLMs) utilize human-produced data, which could limit the capability of any aligned LLM to human level. Label refinement and weak training have emerged as promising strategies to address this superalignment problem. In this work, we adopt probabilistic assumptions commonly used to study label refinement and analyze whether refinement can be outperformed by alternative approaches, including computationally intractable oracle methods. We show that both weak training and label refinement suffer from irreducible error, leaving a performance gap between label refinement and the oracle. These results motivate future research into developing alternative methods for weak to strong generalization that synthesize the practicality of label refinement or weak training and the optimality of the oracle procedure.