  Supplementary Training on Intermediate Labeled-data Tasks (STILTs) is a
widely applied technique, which first fine-tunes the pretrained language models
on an intermediate task before on the target task of interest. While STILTs is
able to further improve the performance of pretrained language models, it is
still unclear why and when it works. Previous research shows that those
intermediate tasks involving complex inference, such as commonsense reasoning,
work especially well for RoBERTa. In this paper, we discover that the
improvement from an intermediate task could be orthogonal to it containing
reasoning or other complex skills -- a simple real-fake discrimination task
synthesized by GPT2 can benefit diverse target tasks. We conduct extensive
experiments to study the impact of different factors on STILTs. These findings
suggest rethinking the role of intermediate fine-tuning in the STILTs pipeline.
