arXiv Computation and Language September 18, 2026 By David Ponce, Thierry Etchegoyhen, Javier Del Ser

GeLaCo: An Evolutionary Approach to Layer Compression

Read the original on arXiv Computation and Language →

GeLaCo is an evolutionary method for compressing large language models by collapsing layers through parametrized weight merging. It uses population-based search with a fitness function that balances similarity of residual updates and language modeling KL divergence, enabling both single and multi-objective compression. The approach yields Pareto-optimal trade-offs between compression and quality, outperforming existing methods in perplexity and generative evaluations.

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