arXiv:2605.15491v2 Announce Type: replace Abstract: Layer pruning removes entire Transformer decoder blocks from large language models, but introduces a mismatch between the hidden state received by the next surviving layer and the distribution it was trained to process, leading to significant performance degradation. We propose Ghosted Layers, a training-free recovery module that addresses this issue by solving a boundary activation alignment problem. Our method derives a closed-form optimal linear operator from a small calibration set to reconstruct the activation discrepancy introduced by t
The rapid advancement and deployment of large language models (LLMs) are pushing researchers to find more efficient methods for model maintenance and deployment, especially after pruning.
This development offers a method to recover performance in pruned LLMs without extensive retraining, directly impacting the cost-effectiveness and accessibility of large AI models.
Previously, layer pruning often led to significant performance degradation requiring expensive retraining; now, 'Ghosted Layers' propose a training-free recovery mechanism, making model compression more viable.
- · AI developers
- · Cloud providers
- · Edge AI computing
- · Startups deploying LLMs
- · Inefficient model compression techniques
- · High-compute training operations for post-pruning models
Reduced computational costs and time for deploying smaller, yet still effective, large language models.
Increased adoption of LLMs in resource-constrained environments due to improved efficiency and reduced overhead.
Acceleration of AI agent development as more performant models become accessible for specialized, leaner applications.
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Read at arXiv cs.LG