arXiv:2510.14444v3 Announce Type: replace Abstract: Post-training pruning can substantially reduce LLM inference costs, but it often degrades quality unless the remaining weights are adapted. Since global retraining is expensive at LLM scale, recent work has largely focused on increasingly sophisticated pruning criteria that aim to select better sparsity patterns without adaptation. We revisit this trade-off through local reconstruction: after pruning, we adapt one subset of the model parameters at a time on a calibration set, training it to match the corresponding intermediate activations of
This research addresses the significant computational cost of large language models (LLMs) which is a present and growing concern for widespread AI adoption and deployment.
Improving LLM compression and efficiency makes advanced AI more accessible and cost-effective, directly impacting the economic viability and scalability of AI-driven applications.
The perceived trade-off between LLM quality and the cost of retraining after pruning is re-evaluated, suggesting that substantial efficiency gains without significant quality degradation are achievable via local reconstruction methods.
- · AI developers
- · Cloud computing providers
- · AI-powered application companies
- · Companies dependent on inefficiently scaled LLMs
- · Providers of highly specialized LLM optimization services
Reduced operational costs for deploying and running large language models.
Increased proliferation of advanced AI capabilities due to lower barriers to entry.
Accelerated innovation in AI models as more complex architectures become economically feasible to deploy.
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Read at arXiv cs.LG