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Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision (2312.09390v1)

Published 14 Dec 2023 in cs.CL

Abstract: Widely used alignment techniques, such as reinforcement learning from human feedback (RLHF), rely on the ability of humans to supervise model behavior - for example, to evaluate whether a model faithfully followed instructions or generated safe outputs. However, future superhuman models will behave in complex ways too difficult for humans to reliably evaluate; humans will only be able to weakly supervise superhuman models. We study an analogy to this problem: can weak model supervision elicit the full capabilities of a much stronger model? We test this using a range of pretrained LLMs in the GPT-4 family on NLP, chess, and reward modeling tasks. We find that when we naively finetune strong pretrained models on labels generated by a weak model, they consistently perform better than their weak supervisors, a phenomenon we call weak-to-strong generalization. However, we are still far from recovering the full capabilities of strong models with naive finetuning alone, suggesting that techniques like RLHF may scale poorly to superhuman models without further work. We find that simple methods can often significantly improve weak-to-strong generalization: for example, when finetuning GPT-4 with a GPT-2-level supervisor and an auxiliary confidence loss, we can recover close to GPT-3.5-level performance on NLP tasks. Our results suggest that it is feasible to make empirical progress today on a fundamental challenge of aligning superhuman models.

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Analyzing "Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision"

The paper "Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision" addresses a significant challenge in the development of AI systems: aligning superhuman models with weak, potentially flawed supervision. This paper from OpenAI provides insights into the mechanisms by which stronger models can be trained with suboptimal or imperfect labels, proposing a methodology that is critical for future AI system alignment.

Core Problem and Methodology

The authors address a core issue in AI alignment techniques, namely the limitations of reinforcement learning from human feedback (RLHF) as models surpass human comprehension. For future superhuman AI, human evaluability becomes increasingly unreliable. The authors propose and test whether weak supervision can indeed elicit the full potential of stronger models. Utilizing GPT-4 family models, the paper investigates the efficacy of "weak-to-strong generalization" across natural language processing tasks, chess puzzles, and reward modeling tasks for ChatGPT.

Key Findings

  1. Positive Weak-to-Strong Generalization: The authors reveal that strong models consistently outperform weak supervisors when naively finetuned on weak labels. For instance, GPT-4 models generalized effectively beyond GPT-2-level supervision across several NLP tasks, recovering significant capability from weak supervision.
  2. Potential Limitations with Naive Finetuning: Despite positive indications, naive finetuning alone does not recover full performance, especially apparent in reward modeling tasks for ChatGPT. The gaps suggest that relying solely on such techniques may be insufficient for aligning superhuman models.
  3. Proposed Methods to Improve Generalization: The paper demonstrates that certain strategies, such as auxiliary confidence losses and bootstrapping intermediate model sizes, yield significant improvements. For NLP tasks, the confidence loss strategy allowed strong models to achieve nearly the performance of models trained with ground truth supervision, suggesting that encouraging models to reject weak model errors can lead to better generalization.

Implications for AI Alignment

The paper indicates that aligning superhuman models using weak supervision is tractable but requires methodological improvements. The ability to elicit full capabilities of strong models from weaker supervisory input highlights an empirical path to addressing superalignment challenges. This exploration serves as a foundational step, suggesting a new direction for alignment techniques that do not yet rely on a complete understanding of human values or narrowly defined tasks.

Future Directions and Scaling Concerns

The paper sets the stage for further research into refining these approaches. There is scope for exploring diverse weak supervision forms and understanding how specific weak label biases affect model generalization. Moreover, the sensitivity of methods to optimization pressures and further development of unsupervised finetuning for task saliency adjustment are promising areas.

Understanding the limits and potential of weak supervision in model alignment will be critical as AI systems become more advanced. The paper suggests not only practical alignment steps for current AI systems but also strategies to anticipate and address future alignment challenges with superhuman models.

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Authors (12)
  1. Collin Burns (11 papers)
  2. Pavel Izmailov (26 papers)
  3. Jan Hendrik Kirchner (4 papers)
  4. Bowen Baker (12 papers)
  5. Leo Gao (16 papers)
  6. Leopold Aschenbrenner (1 paper)
  7. Yining Chen (35 papers)
  8. Adrien Ecoffet (10 papers)
  9. Manas Joglekar (14 papers)
  10. Jan Leike (49 papers)
  11. Ilya Sutskever (58 papers)
  12. Jeff Wu (11 papers)
Citations (193)
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