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Model collapse

2 open-access journal articles on model collapse, published in 2026 in Life of Research. Every record is free to read and download, with a permanent link and a ready-made citation.

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Journal article

Which Signal Did the Synthesis Import? Information, Re-presentation and Selection in Synthetic Pretraining, Why 'the Verifier Is Doing the Work' Holds Only Where Correctness Is the Target, and the Controls That Would Assign the Credit

The expectation that language models will keep improving after the supply of human text runs out rests on a claim about synthetic data: that a model, or a family of models, can produce training text worth more than the text it was trained on. A competing reading holds that no synthetic-data…

Cite Related research LOR-00109 · 6 views · 6 downloads
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Journal article

Does a Model Forget Differently When the Data Is Its Own? RL's Retention Advantage and Model Collapse Are Claims About the Same Loop, and No Study Has Measured Both

Two literatures make claims about what happens when a language model trains on data that resembles its own distribution. One asks whether reinforcement learning forgets a model's prior capabilities less than supervised fine-tuning does, and answers yes: on-policy RL is implicitly biased toward…

Cite Related research LOR-00052 · 8 views · 31 downloads
PDF9.3 MB

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