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The Superalignment of Superhuman Intelligence with Large Language Models

Minlie Huang, Yingkang Wang, Shiyao Cui, Pei Ke, Jie Tang

TL;DR

The paper tackles the safety and alignment challenges of superhuman AI by formalizing superalignment as learning from noisy, scalable feedback when tasks exceed human annotation capacity. It situates superalignment within the LM/MLM learning pipeline and introduces a three-module framework—attacker, learner, critic—to enable automatic, scalable alignment with minimal human supervision. Key contributions include articulating weak-to-strong generalization, scalable oversight, and evaluation as core problems, and surveying related approaches such as self-alignment and self-refinement as precursors. The work emphasizes future directions in identifying emergent risks and pursuing multi-dimensional, culturally aware alignment to ensure robust, trustworthy superhuman models.

Abstract

We have witnessed superhuman intelligence thanks to the fast development of large language models and multimodal language models. As the application of such superhuman models becomes more and more popular, a critical question arises here: how can we ensure superhuman models are still safe, reliable and aligned well to human values? In this position paper, we discuss the concept of superalignment from the learning perspective to answer this question by outlining the learning paradigm shift from large-scale pretraining, supervised fine-tuning, to alignment training. We define superalignment as designing effective and efficient alignment algorithms to learn from noisy-labeled data (point-wise samples or pair-wise preference data) in a scalable way when the task becomes very complex for human experts to annotate and the model is stronger than human experts. We highlight some key research problems in superalignment, namely, weak-to-strong generalization, scalable oversight, and evaluation. We then present a conceptual framework for superalignment, which consists of three modules: an attacker which generates adversary queries trying to expose the weaknesses of a learner model; a learner which will refine itself by learning from scalable feedbacks generated by a critic model along with minimal human experts; and a critic which generates critics or explanations for a given query-response pair, with a target of improving the learner by criticizing. We discuss some important research problems in each component of this framework and highlight some interesting research ideas that are closely related to our proposed framework, for instance, self-alignment, self-play, self-refinement, and more. Last, we highlight some future research directions for superalignment, including identification of new emergent risks and multi-dimensional alignment.

The Superalignment of Superhuman Intelligence with Large Language Models

TL;DR

The paper tackles the safety and alignment challenges of superhuman AI by formalizing superalignment as learning from noisy, scalable feedback when tasks exceed human annotation capacity. It situates superalignment within the LM/MLM learning pipeline and introduces a three-module framework—attacker, learner, critic—to enable automatic, scalable alignment with minimal human supervision. Key contributions include articulating weak-to-strong generalization, scalable oversight, and evaluation as core problems, and surveying related approaches such as self-alignment and self-refinement as precursors. The work emphasizes future directions in identifying emergent risks and pursuing multi-dimensional, culturally aware alignment to ensure robust, trustworthy superhuman models.

Abstract

We have witnessed superhuman intelligence thanks to the fast development of large language models and multimodal language models. As the application of such superhuman models becomes more and more popular, a critical question arises here: how can we ensure superhuman models are still safe, reliable and aligned well to human values? In this position paper, we discuss the concept of superalignment from the learning perspective to answer this question by outlining the learning paradigm shift from large-scale pretraining, supervised fine-tuning, to alignment training. We define superalignment as designing effective and efficient alignment algorithms to learn from noisy-labeled data (point-wise samples or pair-wise preference data) in a scalable way when the task becomes very complex for human experts to annotate and the model is stronger than human experts. We highlight some key research problems in superalignment, namely, weak-to-strong generalization, scalable oversight, and evaluation. We then present a conceptual framework for superalignment, which consists of three modules: an attacker which generates adversary queries trying to expose the weaknesses of a learner model; a learner which will refine itself by learning from scalable feedbacks generated by a critic model along with minimal human experts; and a critic which generates critics or explanations for a given query-response pair, with a target of improving the learner by criticizing. We discuss some important research problems in each component of this framework and highlight some interesting research ideas that are closely related to our proposed framework, for instance, self-alignment, self-play, self-refinement, and more. Last, we highlight some future research directions for superalignment, including identification of new emergent risks and multi-dimensional alignment.

Paper Structure

This paper contains 15 sections, 4 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Definition of Superalignment from the Learning Perspective
  3. Learning paradigm of large-scale pretraining
  4. Alignment training of large-scale language models
  5. Superalignment of large-scale language models
  6. Key Research Problems in Superalignment
  7. Weak-to-strong generalization
  8. Scalable oversight
  9. Evaluation
  10. A Framework to Realize Superalignment
  11. Attacker: Discovering the weaknesses of LLMs automatically
  12. Learner: Learning from scalable feedbacks
  13. Critic: Generating scalable, faithful, and learnable critics
  14. Realization of the superalignment framework
  15. Conclusion and Future Directions

Figures (1)

  • Figure 1: A conceptual framework for superalignment. Attacker generates adversary queries so that Learner may fail to produce high-quality responses; Learner will be continuously improved by learning from scalable feedbacks generated from Critic or from minimal human feedbacks whenever necessary; and Critic generates explanations, feedbacks, or reasons given a query from the attacker and a response from the learner as input. Starting from some seed input, the pipeline can be automatically iterated.