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

arXiv:2610.07606v1 Announce Type: new 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;

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