Learning Decision-Stump Thresholds in Context: Dynamics of Softmax Attention
Estimating a decision threshold requires locating observations near an unknown boundary. We study how gradient-based pretraining learns this statistical rule in a two-parameter softmax-attention model with a fixed feature and inequality direction. Pretraining uses labeled contexts and their true thresholds; a fresh threshold must be inferred from context alone. Under a large-resolution initialization, constant-step gradient descent on $m$ tasks with $n$ examples each produces a frozen estimator with error $\widetilde O((m\wedge n)^{-1}+N^{-1})$ for each fixed interior threshold and every fresh-context size $N$. The two terms separate finite-pretraining accuracy from fresh-context localiza...
| Authors | Hong Ha Le, Jackie Lok, Atsushi Nitanda et al. |
|---|---|
| Primary category | stat.ML |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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