Limitations of refinement methods for weak to strong generalization
This work highlights limitations in current weak-to-strong generalization techniques for superalignment, motivating new methods to bridge the gap between practicality and optimality.
The paper analyzed label refinement and weak training methods for aligning large language models, showing that both suffer from irreducible error and cannot match the performance of an optimal oracle, leaving a performance gap.
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.