Title: Predicting Weak-to-Strong Generalization from Latent Representations
Primary area: interpretability and explainable AI
Keywords: AI Safety, Weak-to-strong generalization, Mechanistic Interpretability

Abstract: AI alignment seeks to align models with human values such as helpfulness and honesty, yet humans may be unable to supervise on tasks exceeding human capabilities. Weak-to-strong generalization (WSG) has been proposed as a proxy for studying this problem, where a weaker model stands in for human supervision and alignment of a stronger model. While prior work provides evidence of WSG success, i.e. the strong model outperforming the weak supervision signal, prior tasks suffer from train-test contamination or rely on oversimplified linear models. We introduce a clean toy-testbed where transformer model pairs are pretrained on different rule variants of Othello and Tic-Tac-Toe, then the stronger model is finetuned on output from the weaker model. 
It has been hypothesized that WSG works when the strong model learns how to leverage its superior features. While there has been prior theoretical support, we provide the first empirical evidence for this on transformers. In Othello, the strong student model surpasses the weaker teacher if and only if it has better board representations. Across 111 WSG pairs and 6 game rules, we find a 0.85 Spearman correlation between WSG success and superior board representations in the strong model as measured by linear probes. Our work is a proof-of-concept by analyzing a toy task. By open-sourcing our experiments, we hope to accelerate research on understanding when WSG succeeds.

TL;DR: We train generative models on board games and probe their internal representations to predict weak-to-strong generalization success.