[2606.23670] Tapered Language Models

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Computer Science > Machine Learning

arXiv:2606.23670 (cs)

[Submitted on 22 Jun 2026]

Title:Tapered Language Models

Authors:Reza Bayat, Ali Behrouz, Aaron Courville<br>View a PDF of the paper titled Tapered Language Models, by Reza Bayat and 2 other authors

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Abstract:Modern language models, including transformer, recurrent, and memory-based variants, share a common chassis: a stack of identical layers in which parameters are allocated uniformly across depth. This is a default inherited from the original transformer and largely unchanged since, yet a growing body of evidence suggests that layers contribute non-uniformly to the final output, with later layers refining the residual stream rather than transforming it. We ask whether parameter capacity should reflect this asymmetry. Our controlled experiment shows that, under a fixed budget, allocating more capacity to earlier layers and less to later layers improves perplexity over a uniform-width baseline, while the reverse allocation hurts. Building on this result, we introduce Tapered Language Models (TLMs), an architectural principle in which a parameter-bearing component is monotonically tapered across depth under a fixed total budget. MLPs are the natural site for this instantiation: they dominate parameter count across all modern LM families and expose width as a single, clean axis of variation. Across three model scales and four architectures (Transformer, Gated Attention, Hope-attention, and Titans), tapering MLP width via a smooth cosine schedule consistently improves perplexity and downstream benchmark performance over uniform baselines, at no additional parameter or compute cost. These findings establish depth-aware capacity allocation as a simple, architecture-agnostic axis of language model design, a free lever hidden in plain sight.

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

Cite as:<br>arXiv:2606.23670 [cs.LG]

(or<br>arXiv:2606.23670v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.23670

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Reza Bayat [view email]<br>[v1]<br>Mon, 22 Jun 2026 17:56:25 UTC (573 KB)

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