Structural Sensitivity in Compressed Transformers: Relative Error Propagation and Layer Removal
Compressing transformer weights makes large language models cheaper to deploy. But each layer's compression introduces an error. These errors accumulate as the signal passes through later layers, and how they accumulate is not well understood. We measure this directly: at each layer, we take the ratio of output to input error, calling it rho. A value below one means the layer absorbs the error; above one means it grows. Computing rho on six transformers (117M to 8B parameters) yields three findings. (i) Errors at layer t scale downstream by the product of later rho values, predicting representation drift (Spearman r = -0.44, p < 10^-4). This explains why compressing early layers hurts mor...
| Authors | Abhinaba Basu, Kumkum Basu, Koushik Deb |
|---|---|
| Primary category | cs.LG |
| Published | 22 March 2026 |
| Last updated at source | 7 May 2026 |
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