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About this paper
Damien Teney, Liangze Jiang, Zachary Shinnick, Hemanth Saratchandran, Simon Lucey
The authors test whether learned nonlinearities tuned on one data source improve transformer training on another. They train spline functions on source data, freeze them, and reuse them in a fresh model. Across seven datasets, target-tuned variants improve training efficiency or final performance, but transfer varies. In a frozen-layer test, MATH-SUBSET accuracy rises from 22.3% to 39.5%. Results cover small-model validation.
Summary by alphaXiv.
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