Half the Nonlinearity Is Wasted: Measuring and Reallocating the Transformer's MLP Budget
We investigate when transformer MLP nonlinearity is actually necessary. A gate with $d+1$ parameters decides when to replace the full MLP with a linear surrogate. Through systematic investigation across six models (162M-2.8B parameters), two architectures, and three corpora, we establish that nonlinearity need cannot be predicted from token identity: cross-corpus correlation is zero ($r < 0.05$). The routing decision is fully contextual. Despite weak per-instance predictability, the gate exploits a heavily skewed distribution where most MLP computations are near-linear, achieving 25-56% linear routing at <1% perplexity cost in GPT-2. In GPT-2 Large, 11 of 36 layers beat baseline with gati...
| Authors | Peter Balogh |
|---|---|
| Primary category | cs.LG |
| Published | 3 March 2026 |
| Last updated at source | 6 March 2026 |
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