# IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining

DevFeed: [IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining](<https://devfeed.tech/articles/idea-prune-an-integrated-enlarge-and-prune-pipeline-in-generative-language-model-pretraining-6729.md>)

Original publisher: [Read original article](<https://machinelearning.apple.com/research/idea-prune-pipeline>)

Published: 2026-08-26T00:00:00Z

Content type: article

Language: en

Sources: [Apple Machine Learning Research](<https://devfeed.tech/sources/apple-machine-learning-research.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [compression and generalization](<https://devfeed.tech/topics/compression-and-generalization.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>)

Tags: [compression](<https://devfeed.tech/tags/compression.md>), [generative](<https://devfeed.tech/tags/generative.md>), [inference](<https://devfeed.tech/tags/inference.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [model](<https://devfeed.tech/tags/model.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

This paper presents IDEA Prune, an integrated enlarge-and-prune pipeline for generative language model pretraining. It combines enlarged-model training, iterative structured pruning, and recovery under one cosine annealing learning-rate schedule, with experiments compressing 2.8B models to 1.3B.

## Source excerpt

Recent advancements in large language models have intensified the need for efficient and deployable models within limited inference budgets. Structured pruning pipelines have shown promise in token efficiency compared to training target-size models from scratch. In this paper, we advocate incorporating enlarged model pretraining, which is often ignored in previous works, into pruning. We study the enlarge-and-prune pipeline as an integrated system to address two critical questions: whether it is worth pretraining an enlarged model even when the model is never deployed, and how to optimize the...