Tapered Language Models: Optimizing Parameter Allocation Across Model Depth

Researchers propose a departure from the traditional uniform layer architecture in Large Language Models (LLMs), suggesting that parameter capacity should be distributed non-uniformly to better align with how layers actually contribute to the residual stream.

The Limitations of Uniform Layer Architecture

Since the inception of the original Transformer architecture, the industry standard for language models—including recurrent and memory-based variants—has been to utilize a "common chassis." This design consists of a stack of identical layers where parameters are allocated uniformly across the entire depth of the network. This architectural symmetry assumes that every layer requires the same capacity to process information.

The Case for Non-Uniform Capacity

Recent empirical evidence suggests that this uniform distribution is suboptimal. Analysis indicates that different layers contribute non-uniformly to the final output. Specifically, there is a growing observation that later layers in a model tend to refine the residual stream rather than performing the heavy-duty transformations characteristic of the earlier stages of the network.

Proposed Approach: Tapering Parameter Capacity

The authors—Reza Bayat, Ali Behrouz, and Aaron Courville—investigate whether parameter capacity should be "tapered." By adjusting the allocation of parameters across the depth of the model, the goal is to align the model's capacity with the actual functional requirements of each layer, potentially reducing redundancy and improving efficiency without sacrificing performance.

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