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Human Supervision as an Information Bottleneck: A Unified Theory of Error Floors in Human-Guided Learning

arXiv:2602.23446v1 Announce Type: cross Abstract: Large language models are trained primarily on human-generated data and feedback, yet they exhibit persistent errors arising from annotation noise, subjective preferences, and the limited expressive bandwidth of natural language. We argue that these limitations reflect structural properties of the supervision channel rather than model scale or optimization. We develop a unified theory showing that whenever the human supervision channel is not sufficient for a latent evaluation target, it acts as an information-reducing channel that induces a strictly positive excess-risk floor for any learner dominated by it. We formalize this Human-Bounded Intelligence limit and show that across six complementary frameworks (operator theory, PAC-Bayes, information theory, causal inference, category theory, and game-theoretic analyses of reinforcement learning from human feedback), non-sufficiency yields strictly positive lower bounds arising from the

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Alejandro Rodriguez Dominguez · · 1 min read · 2 views
#cs.LG #cs.AI

arXiv:2602.23446v1 Announce Type: cross Abstract: Large language models are trained primarily on human-generated data and feedback, yet they exhibit persistent errors arising from annotation noise, subjective preferences, and the limited expressive bandwidth of natural language. We argue that these limitations reflect structural properties of the supervision channel rather than model scale or optimization. We develop a unified theory showing that whenever the human supervision channel is not sufficient for a latent evaluation target, it acts as an information-reducing channel that induces a strictly positive excess-risk floor for any learner dominated by it. We formalize this Human-Bounded Intelligence limit and show that across six complementary frameworks (operator theory, PAC-Bayes, information theory, causal inference, category theory, and game-theoretic analyses of reinforcement learning from human feedback), non-sufficiency yields strictly positive lower bounds arising from the same structural decomposition into annotation noise, preference distortion, and semantic compression. The theory explains why scaling alone cannot eliminate persistent human-aligned errors and characterizes conditions under which auxiliary non-human signals (e.g., retrieval, program execution, tools) increase effective supervision capacity and collapse the floor by restoring information about the latent target. Experiments on real preference data, synthetic known-target tasks, and externally verifiable benchmarks confirm the predicted structural signatures: human-only supervision exhibits a persistent floor, while sufficiently informative auxiliary channels strictly reduce or eliminate excess error.

Executive Summary

This article presents a unified theory explaining why human-supervised learning models exhibit persistent errors, despite scaling and optimization efforts. The authors argue that these limitations stem from structural properties of the supervision channel, not model scale or optimization. They formalize the Human-Bounded Intelligence limit, demonstrating that non-sufficiency of human supervision induces a strictly positive excess-risk floor. The theory is supported by six complementary frameworks and confirmed by experiments across various tasks. The authors propose that auxiliary non-human signals can increase effective supervision capacity and collapse the floor. This research has significant implications for the development of more accurate and reliable human-guided learning models.

Key Points

  • ▸ Human supervision acts as an information-reducing channel, inducing a strictly positive excess-risk floor.
  • ▸ The Human-Bounded Intelligence limit is a structural property of the supervision channel, not a model-specific issue.
  • ▸ Auxiliary non-human signals can increase effective supervision capacity and collapse the excess-risk floor.

Merits

Strength in Theory Development

The authors develop a comprehensive and unified theory explaining the limitations of human-supervised learning models, which is a significant contribution to the field.

Empirical Support

The experiments across various tasks and frameworks provide strong empirical evidence supporting the proposed theory.

Demerits

Limited Generalizability

The findings may not generalize to all human-supervised learning tasks, particularly those with unique requirements or characteristics.

Complexity of Implementation

The integration of auxiliary non-human signals may be challenging and require significant modifications to existing learning models.

Expert Commentary

The article presents a well-developed and comprehensive theory explaining the limitations of human-supervised learning models. The empirical support from various experiments and frameworks strengthens the findings, suggesting that the Human-Bounded Intelligence limit is a structural property of the supervision channel. While the research has significant implications for the development of more accurate and reliable human-guided learning models, the limited generalizability and complexity of implementation are important considerations. The findings have connections to model interpretability and explainability and human-AI collaboration, highlighting the importance of understanding and addressing the limitations of human supervision in learning models.

Recommendations

  • ✓ Future research should explore the development of more effective auxiliary non-human signals that can improve the effectiveness of human supervision.
  • ✓ The findings of this research should be applied to various applications of human-guided learning models, such as decision-making and recommendation systems.

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