Crown, Frame, Reverse: Layer-Wise Scaling Variants for LLM Pre-Training

• URL: http://arxiv.org/abs/2509.06518v1
• Date: Mon, 08 Sep 2025 10:24:19 GMT
• Title: Crown, Frame, Reverse: Layer-Wise Scaling Variants for LLM Pre-Training
• Authors: Andrei Baroian, Kasper Notebomer,
• Abstract summary: Transformer-based language models traditionally use uniform (isotropic) layer sizes, yet they ignore the diverse functional roles that different depths can play and their computational capacity needs.<n>We introduce three new literature variants - Framed, Reverse, and Crown - that redistribute FFN widths and attention heads via two or three-point linear in the pre-training stage.<n>We present the first systematic ablation of LWS and its variants, on a fixed budget of 180M parameters, trained on 5B tokens.<n>All models converge to similar losses and achieve better performance compared to an equal-cost isotropic baseline, without a substantial decrease in training
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Transformer-based language models traditionally use uniform (isotropic) layer sizes, yet they ignore the diverse functional roles that different depths can play and their computational capacity needs. Building on Layer-Wise Scaling (LWS) and pruning literature, we introduce three new LWS variants - Framed, Reverse, and Crown - that redistribute FFN widths and attention heads via two or three-point linear interpolation in the pre-training stage. We present the first systematic ablation of LWS and its variants, on a fixed budget of 180M parameters, trained on 5B tokens. All models converge to similar losses and achieve better performance compared to an equal-cost isotropic baseline, without a substantial decrease in training throughput. This work represents an initial step into the design space of layer-wise architectures for pre-training, but future work should scale experiments to orders of magnitude more tokens and parameters to fully assess their potential.

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