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

Abstract:This paper establishes a theoretical framework for vertical adaptive layer skipping, proving three foundational results: (i) an Expected FLOPs formula (theorem 2) giving a closed-form expression for the computational cost of arbitrary per-sample skip schedules as a function of layer-wise skip probabilities; (ii) function-space superset (theorem 10) and strict inclusion (theorem 11) theorems showing that skip-layer models are strictly contained in---yet meaningfully approximate---the full-layer function space, with an explicit separating example; and (iii) a structural duality between VALSE and Mixture-of-Experts architectures (proposition 6), positioning vertical depth-wise sparsity as the orthogonal counterpart to horizontal width-wise sparsity. Building on this theory, we propose VALSE (Vertical Adaptive Layer Skipping for Efficiency), a per-sample, non-contiguous layer skipping method: a lightweight difficulty estimator scores each input from the first few layers, and per-layer gates selectively skip redundant layers---including arbitrary middle layers while retaining deeper ones---so that only the necessary depth is activated for each input, whose feasibility is preliminarily assessed at prototype scale.

Artificial Intelligence (cs.AI)

arXiv:2610.07606 [cs.AI]

(or arXiv:2610.07606v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.07606 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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