Improving Weak-to-Strong Generalization with Scalable Oversight and Ensemble Learning

TL;DR

This work advances superalignment research by operationalizing Weak-to-Strong Generalization (W2SG) through two phases: a Phase 1 that strengthens weak supervision via ensemble learning and scalable oversight, and a Phase 2 that introduces a recursively updated automated alignment evaluator to sustain alignment as models approach superintelligence. It systematically evaluates ensemble techniques (bagging and boosting) and scalable oversight modalities (interaction and debate), demonstrating that ensemble-based and interaction-enhanced supervision can meaningfully improve weak-to-strong generalization on the SciQ task, while debate-based methods show mixed results. The study also extends W2SG to in-context learning (ICL) scenarios, showing improved effectiveness when using SO and confidence-aware prompts, and highlights the importance of contextual example selection (Top-K) for ICL-based W2SG. Collectively, the findings suggest a practical pathway toward scalable, aligned AI via phased supervision upgrades, while outlining limitations and future directions, including calibration of auto evaluators and more robust task design for recursive alignment.

Abstract

This paper presents a follow-up study to OpenAI's recent superalignment work on Weak-to-Strong Generalization (W2SG). Superalignment focuses on ensuring that high-level AI systems remain consistent with human values and intentions when dealing with complex, high-risk tasks. The W2SG framework has opened new possibilities for empirical research in this evolving field. Our study simulates two phases of superalignment under the W2SG framework: the development of general superhuman models and the progression towards superintelligence. In the first phase, based on human supervision, the quality of weak supervision is enhanced through a combination of scalable oversight and ensemble learning, reducing the capability gap between weak teachers and strong students. In the second phase, an automatic alignment evaluator is employed as the weak supervisor. By recursively updating this auto aligner, the capabilities of the weak teacher models are synchronously enhanced, achieving weak-to-strong supervision over stronger student models.We also provide an initial validation of the proposed approach for the first phase. Using the SciQ task as example, we explore ensemble learning for weak teacher models through bagging and boosting. Scalable oversight is explored through two auxiliary settings: human-AI interaction and AI-AI debate. Additionally, the paper discusses the impact of improved weak supervision on enhancing weak-to-strong generalization based on in-context learning. Experiment code and dataset will be released at https://github.com/ADaM-BJTU/W2SG.

Figures (9)

• Figure 1: Task difficulty: AI can do vs. overseer can evaluate .
• Figure 2: The roadmap of superalignment via weak-to-strong generalization.
• Figure 3: Illustration of the proposed W2SG-based superalignment roadmap.
• Figure 4: Weak models with different training set sampling.
• Figure 6: Weak models with different feature layer combination.