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February 8, 2026Open Access

Loss Distribution Collapse: A Structural Theory of Dataset Degradation

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Authors

BNBato Naidanov

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Overview

The paper reveals degradation in datasets and models during recursive training, suggesting stability requires tail mass preservation.

Key Points

  • The aim is to understand how recursive training leads to dataset and model degradation, emphasizing the role of loss distribution.
  • Introduced a structural theory of dataset degradation
  • Formalized degradation as an iterative distributional transformation
  • Supported with controlled experiments on discrete distributions, continuous models, and language models
  • Analyzed stability using metrics like KL divergence, entropy, and tail mass
  • Identified that recursive self-training sharpens low-loss samples, causing rare cases to vanish
  • Common mitigation strategies fail to address the root cause of model collapse
  • Highlighted the need for mechanisms to preserve loss distribution for effective prevention

Cite This Study

Bato Naidanov (2026) studied this question.

synapsesocial.com/papers/698829410fc35cd7a8849744https://doi.org/10.5281/zenodo.18498819
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