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April 1, 2024Open Access

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

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Authors

MGMatthias GerstgrasserStanford UniversityRSRylan SchaefferChampalimaud FoundationADApratim DeyStanford University

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Overview

Empirical analysis demonstrates that accumulating data prevents model collapse in generative models, highlighting its importance.

Key Points

  • Model collapse occurs when performance degrades across iterations, leading to useless outputs with generative models.
  • In experiments, accumulating data kept test error from increasing indefinitely, unlike when data was replaced over time.
  • Analysis reveals accumulating datasets leads to finite upper bounds on test errors, significantly improving model reliability over iterations. Both theoretical and empirical evidence supports accumulation as essential for effective training strategies.

Cite This Study

Gerstgrasser et al. (2024) studied this question.

synapsesocial.com/papers/68e70edbb6db643587687e41https://doi.org/10.48550/arxiv.2404.01413
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1When Models Don't Collapse: On the Consistency of Iterative MLE2025
  2. 2ForTIFAI: Fending Off Recursive Training Induced Failure for AI Model Collapse2025
  3. 3How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse2024 · 3 citations
  4. 4Multi-modal Synthetic Data Training and Model Collapse: Insights from VLMs and Diffusion Models2025
  5. 5Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models2026 · 1 citations