这篇论文发现了一个简单技巧：同等算力下，把更多参数分给前几层、少给后几层，模型效果就能更好，试了多种架构都管用。
当前语言模型在深度上均匀分配参数，但研究表明各层贡献不同。该论文在固定预算下实验发现，将更多参数分配给前层、减少后层可以改进困惑度。提出Tapered Language Models（TLMs），通过余弦调度平滑锥形化MLP宽度。在Transformer、Gated Attention、Hope-attention和Titans四种架构上，三个模型尺度均一致提升困惑度和下游基准性能，且不增加参数或计算量。
Tapered Language Models
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.