# Recursive Improvement: AI Singularity Or Just Benchmark Saturation?

DevFeed: [Recursive Improvement: AI Singularity Or Just Benchmark Saturation?](<https://devfeed.tech/articles/recursive-improvement-ai-singularity-or-just-benchmark-saturation-33455.md>)

Original publisher: [Read original article](<https://timkellogg.me/blog/2025/02/12/recursive-improvement>)

Published: 2025-02-12T00:00:00Z

Content type: opinion

Language: en

Sources: [Tim Kellogg](<https://devfeed.tech/sources/tim-kellogg.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [llms](<https://devfeed.tech/tags/llms.md>), [paper](<https://devfeed.tech/tags/paper.md>), [self-improvement](<https://devfeed.tech/tags/self-improvement.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

This opinion article examines a paper describing recursive self-improvement for large language models: models generate problems and answers, filter results through majority voting, and train on the resulting corpus. It argues that the approach may extend performance on incremental, objectively verifiable tasks, while facing limitations with ambiguous problems, creative writing, and cost.

## Source excerpt

A fascinating new paper shows that LLMs can recursively self-improve. They can be trained on older versions of themselves and continuously get better. This immediately made me think, "this is it, it's the AI singularity", that moment when AI is able to autonomously self-improve forever and become a... (well that sentence can end a lot of ways)