IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining
Updated 2 h ago · first seen 11 Sept 2026
paper_01M294AHMZVSPNP96ZR27XZWBH
- Published
- 26 Aug 2026
- T1 · 2 h ago
- arXiv
- 2503.05920
- T1 · 2 h ago
Abstract
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…
Authors 7
Yixiao Li, Xianzhi Du, Ajay Jaiswal, Tao Lei, Tuo Zhao, Chong Wang, Jianyu Wang
Specification
- Paper
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
- arXiv id
- 2503.05920
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
- Published
- 26 Aug 2026
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
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6
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T16
Freshest observation
2 h ago
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- Apple
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 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… | → current | current | Apple Machine Learning ResearchT1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
- New paperPaperIDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model PretrainingApple
New paper: IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining (Apple)
apple_ml
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| Apple Machine Learning Research | machinelearning.apple.com/rss.xml | feed | T1· Official | 2 h ago | 1 |
| Apple Machine Learning Research | machinelearning.apple.com/research/idea-prune-pipeline | paper_page | T1· Official | 2 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.