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Debate Helps Weak-to-Strong Generalization

Hao Lang, Fei Huang, Yongbin Li

TL;DR

The paper addresses the challenge of aligning future superhuman models under weak human supervision by combining scalable oversight with weak-to-strong generalization. It introduces a debate-based mechanism where strong models generate long-form arguments, which are used to train a robust ensemble of weak supervisors, whose aggregated labels steer a strong model. Empirical results on four OpenAI weak-to-strong NLP benchmarks show that this hybrid approach yields positive performance-gap recovery (PGR) and superior accuracy compared to baselines, suggesting that debate can extract trustworthy information from untrustworthy strong models and that weak-model ensembles provide robust supervision. The findings indicate that integrating SO and W2SG can significantly improve alignment and generalization in settings where human supervision is scarce relative to model capability, albeit with notable computational costs and design considerations for ensemble diversity and debate length.

Abstract

Common methods for aligning already-capable models with desired behavior rely on the ability of humans to provide supervision. However, future superhuman models will surpass the capability of humans. Therefore, humans will only be able to weakly supervise superhuman models. This expected deficiency of human evaluation would weaken the safety of future AI systems. Scalable oversight and weak-to-strong generalization are two complementary approaches to tackle this issue. In this paper, we attempt to combine the strengths of these two approaches to further improve alignment. Specifically, we investigate ways of improving human supervision with a strong pretrained model and then supervise the strong model with enhanced weak human supervision. To make iterative empirical progress, we consider an analogy: can we use a strong model to improve weak model supervision and then use it to supervise the strong model? We empirically test it by finetuning a small weak model on ground truth labels with the additional help from a large strong model, and then finetuning the strong model on labels generated by the weak model. We find that debate can assist a weak model in extracting trustworthy information from an untrustworthy strong model, which provides leverage as context on samples when training a weak model. We also show that an ensemble of weak models helps exploit long arguments generated by strong model debaters and obtain a more robust supervision estimate. Extensive experiments on the OpenAI weak-to-strong NLP benchmarks show that the combination approach leads to better alignment, which indicates that debate has the potential to help weak-to-strong generalization.

Debate Helps Weak-to-Strong Generalization

TL;DR

The paper addresses the challenge of aligning future superhuman models under weak human supervision by combining scalable oversight with weak-to-strong generalization. It introduces a debate-based mechanism where strong models generate long-form arguments, which are used to train a robust ensemble of weak supervisors, whose aggregated labels steer a strong model. Empirical results on four OpenAI weak-to-strong NLP benchmarks show that this hybrid approach yields positive performance-gap recovery (PGR) and superior accuracy compared to baselines, suggesting that debate can extract trustworthy information from untrustworthy strong models and that weak-model ensembles provide robust supervision. The findings indicate that integrating SO and W2SG can significantly improve alignment and generalization in settings where human supervision is scarce relative to model capability, albeit with notable computational costs and design considerations for ensemble diversity and debate length.

Abstract

Common methods for aligning already-capable models with desired behavior rely on the ability of humans to provide supervision. However, future superhuman models will surpass the capability of humans. Therefore, humans will only be able to weakly supervise superhuman models. This expected deficiency of human evaluation would weaken the safety of future AI systems. Scalable oversight and weak-to-strong generalization are two complementary approaches to tackle this issue. In this paper, we attempt to combine the strengths of these two approaches to further improve alignment. Specifically, we investigate ways of improving human supervision with a strong pretrained model and then supervise the strong model with enhanced weak human supervision. To make iterative empirical progress, we consider an analogy: can we use a strong model to improve weak model supervision and then use it to supervise the strong model? We empirically test it by finetuning a small weak model on ground truth labels with the additional help from a large strong model, and then finetuning the strong model on labels generated by the weak model. We find that debate can assist a weak model in extracting trustworthy information from an untrustworthy strong model, which provides leverage as context on samples when training a weak model. We also show that an ensemble of weak models helps exploit long arguments generated by strong model debaters and obtain a more robust supervision estimate. Extensive experiments on the OpenAI weak-to-strong NLP benchmarks show that the combination approach leads to better alignment, which indicates that debate has the potential to help weak-to-strong generalization.
Paper Structure (28 sections, 1 equation, 3 figures, 5 tables)

This paper contains 28 sections, 1 equation, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Scalable oversight.
  4. Weak-to-strong generalization.
  5. Ensemble methods.
  6. Preliminaries
  7. 1. Create the weak supervisor.
  8. 2. Train a strong student model.
  9. 3. Train a strong ceiling model.
  10. Methods
  11. Overview
  12. Argument Generation through Debate
  13. Weak Model Ensemble Training
  14. Training Strong Models using Ensembles
  15. Experiments
  16. ...and 13 more sections

Figures (3)

  • Figure 1: Illustration of debate. Illustration of the debate procedure between debater A and debater B.
  • Figure 2: Ablation study on the cardinality of the ensemble. Here, our approach uses debate ensembles.
  • Figure 3: Ablation on the number of turns of debate. Here, our approach uses debate ensembles.