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2505.19046
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When Models Don't Collapse: On the Consistency of Iterative MLE
25 May 2025
Daniel Barzilai
Ohad Shamir
SyDa
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ArXiv (abs)
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Papers citing
"When Models Don't Collapse: On the Consistency of Iterative MLE"
7 / 7 papers shown
Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence
Bingji Yi
Qiyuan Liu
Yuwei Cheng
Haifeng Xu
SyDa
201
0
0
18 Oct 2025
Another Turn, Better Output? A Turn-Wise Analysis of Iterative LLM Prompting
Shashidhar Reddy Javaji
Bhavul Gauri
Zining Zhu
LRM
199
1
0
08 Sep 2025
Position: Model Collapse Does Not Mean What You Think
Rylan Schaeffer
Joshua Kazdan
Alvan Caleb Arulandu
Sanmi Koyejo
741
20
0
05 Mar 2025
A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops
International Conference on Learning Representations (ICLR), 2025
Shi Fu
Yingjie Wang
Yuzhu Chen
Xinmei Tian
Dacheng Tao
372
8
0
26 Feb 2025
Escaping Collapse: The Strength of Weak Data for Large Language Model Training
Kareem Amin
Sara Babakniya
Alex Bie
Weiwei Kong
Umar Syed
Sergei Vassilvitskii
388
5
0
13 Feb 2025
Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World
Joshua Kazdan
Rylan Schaeffer
Apratim Dey
Matthias Gerstgrasser
Rafael Rafailov
D. Donoho
Sanmi Koyejo
612
36
0
22 Oct 2024
Nepotistically Trained Generative-AI Models Collapse
Matyáš Boháček
Hany Farid
329
27
0
20 Nov 2023
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