Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-dataTasks

Give language models a second lesson and they learn better

Try this: teach a language program first, then give it a focused second lesson on a related task, and it starts to work noticeably better.
That extra step makes the system pick up patterns more quickly and generalize to new sentences, so real-world tasks get easier.
It not only raises accuracy but often produces more stable results when you run it again, like fewer surprise swings.
The boost is biggest when you have very little labeled data to learn from, so this helps projects with small teams or tight budgets.
This trick works across different model types, so it isn't just a one-off.
Think of it as a short, smart practice session that turns a good learner into a better one.
For people building apps that read text or answer questions, this means faster improvements and better understanding without huge data farms.
The idea is simple, cheap, and surprisingly powerful — a small change that gives extra training payoffs and helps when you have less data to teach with.

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Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-dataTasks

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