Improving Weak-to-Strong with Scalable Oversight
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A research paper addressing superalignment through weak-to-strong generalization, proposing scalable oversight methods to ensure AI systems remain aligned with human values as they become superhuman.
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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.
Summary
This paper extends OpenAI's Weak-to-Strong Generalization (W2SG) framework for superalignment by proposing methods to improve weak supervision across two phases: developing superhuman models and progressing toward superintelligence. The authors enhance weak supervision quality through scalable oversight techniques (human-AI interaction and AI-AI debate) combined with ensemble learning, reducing the capability gap between weak teachers and strong students. In the second phase, they employ an automatic alignment evaluator as a weak supervisor that recursively updates to maintain alignment as student models become stronger. Initial validation on the SciQ task demonstrates the effectiveness of ensemble methods and scalable oversight approaches.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Alignment | Approach | 91.0 |
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# Improving Weak-to-Strong Generalization with Scalable Oversight and Ensemble Learning
Jitao SangThese authors contributed equally to this work.Corresponding author: jtsang@bjtu.edu.cn
Department of Computer Science
Beijing Jiaotong University
Beijing, China 100044
Yuhang Wang11footnotemark: 1 Department of Computer Science
Beijing Jiaotong University
Beijing, China 100044
Jing Zhang Department of Computer Science
Beijing Jiaotong University
Beijing, China 100044
Yanxu Zhu Department of Computer Science
Beijing Jiaotong University
Beijing, China 100044
Chao Kong Department of Computer Science
Beijing Jiaotong University
Beijing, China 100044
Junhong Ye Department of Computer Science
Beijing Jiaotong University
Beijing, China 100044
Shuyu Wei Department of Computer Science
Beijing Jiaotong University
Beijing, China 100044
Jinlin Xiao Department of Computer Science
Beijing Jiaotong University
Beijing, China 100044
###### 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](https://github.com/ADaM-BJTU/W2SG "").
## 1 Introduction
Reinforcement Learning from Human Feedback (RLHF) is the pivotal solution today in aligning powerful AI models with human intentions and values. It has demonstrated considerable success in aligning advanced language models such as GPT-3.5, GPT-4, and Llama2. RLHF is underpinned by the principle of rewarding behaviors highly regarded by humans while penalizing those deemed inferior, as outlined in se
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