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LLM self-learning doomed to inevitable model collapse, research suggests

By PulseAugur Editorial · [1 sources] ·

A recent paper by Hector Zenil argues that large language models (LLMs) are inherently prone to model collapse when attempting to self-learn. The paper posits that LLMs, as statistical models, will converge on a statistical singularity rather than achieving artificial general intelligence if they rely solely on their own outputs for training. Continuous training with external, human-generated data is necessary to prevent this degradation and maintain model performance. AI

IMPACT Highlights the critical need for external data to prevent LLM degradation and maintain performance.

RANK_REASON Academic paper detailing a theoretical risk of self-training in LLMs.

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM self-learning doomed to inevitable model collapse, research suggests

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Academic paper detailing a theoretical risk of self-training in LLMs.
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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    Why Model Collapse in LLMs is Inevitable With Self-Learning https://hackaday.com/2026/04/29/why-model-collapse-in-llms-is-inevitable-with-self-learning/ # AI #

    Why Model Collapse in LLMs is Inevitable With Self-Learning https://hackaday.com/2026/04/29/why-model-collapse-in-llms-is-inevitable-with-self-learning/ # AI # MachineLearning # LLM

    LINKS hackaday.com/…/why-model-collapse-in-llms…

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