Training a tiny fraction of units boosts learning, but gains fade when training grows
A small set of trainable components can help learning early, but benefits shrink as the set increases.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Xin He, Huan Lao, Xiliang Lu, Shijun Zhang
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
What happened: Researchers looked at networks where most parts stay fixed after start, while a small subset can be trained. They saw early gains from training a few parts, then returns leveled off as more parts are trainable, depending on fit.
Why it matters
Teams can try training a tiny slice first to test gains, since larger training may not always help in practice in real projects.
Most parts stay fixed; only a small part is adjusted to fit the task. Gains come from training a small subset, but they shrink as parts are trained.
A practical takeaway is to test a small initial training set on real data first, then decide if expanding the trainable portion is worth the cost.
What remains uncertain
Results depend on starting settings, task type, data, and how training is done; this is not a universal rule.
Original sources · 1
- Phase Transition from Random Features to Fully Trainable Networks ↗arXiv · 2026-10-22
Check the original paper for its authors, methods, version and access terms.
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