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recursive-training

topic2 events
papersSEP 12 04:00 UTC

arXiv Paper Models Collapse When Multiple AI Systems Train on Each Other's Output

A new arXiv preprint examines how recursive training on AI-generated text leads to model collapse, extending prior work from a single model to settings where many models exchange and train on one another's outputs. The authors analyze how the dynamics play out across a multi-model ecosystem, where each participant learns from a shared pool of synthetic data. The work is a preprint and has not yet been peer reviewed.

model-collapse/synthetic-data/research/training-data
papersSEP 12 04:00 UTC

arXiv paper proposes fragility spectrum for recursive language-model training

A new arXiv preprint examines what happens when text produced by language models is fed back into their own training data, a practice linked to shrinking output diversity. The authors propose a "fragility spectrum" framework to characterize how different training protocols and data mixtures degrade under this recursive loop. The work aims to give researchers a more systematic way to compare which setups hold up and which break down.

arxiv/training-data/model-collapse/research