[2605.05222] Adaptive Computation Depth via Learned Token Routing in Transformers
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Computer Science > Machine Learning
arXiv:2605.05222 (cs)
[Submitted on 18 Apr 2026]
Title:Adaptive Computation Depth via Learned Token Routing in Transformers
Authors:Ahmed Abdelmuniem Abdalla Mohammed
View a PDF of the paper titled Adaptive Computation Depth via Learned Token Routing in Transformers, by Ahmed Abdelmuniem Abdalla Mohammed
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Abstract:Standard transformer architectures apply the same number of layers to every token regardless of contextual difficulty. We present Token-Selective Attention (TSA), a learned per-token gate on residual updates between consecutive transformer blocks. Each gate is a lightweight two-layer multi-layer perceptron (MLP) that produces a continuous halting probability, making the mechanism end-to-end differentiable with 1.7% parameter overhead and no changes to the base architecture. Notably, TSA learns difficulty-proportional routing without any explicit depth pressure: even at $\lambda=0$ (no depth regularisation), the task-loss gradient alone drives the router to skip 20% of token-layer operations. On character-level language modeling, TSA saved 14-23% of token-layer operations (TLOps) across Tiny-Shakespeare and enwik8 at
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