arXiv AI October 7, 2026 By Jia-Dong Zhang

VALSE: Vertical Adaptive Layer Skipping for Efficient Inference in Large Language Models

The paper introduces VALSE, a vertical adaptive layer skipping technique for large language models. It provides a theoretical framework proving an Expected FLOPs formula, showing that skip-layer models are a strict subset yet meaningful approximation of full-layer models, and revealing a duality between VALSE and Mixture-of-Experts architectures. VALSE uses a lightweight difficulty estimator to selectively skip redundant layers per input, enabling efficient inference by activating only necessary depth.

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