Methods › General › Fine-Tuning › Child-Tuning
Child-Tuning
Introduced by Runxin Xu et al. in Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Child-Tuning is a fine-tuning technique that updates a subset of parameters (called child network) of large pretrained models via strategically masking out the gradients of the non-child network during the backward process. It decreases the hypothesis space of the model via a task-specific mask applied to the full gradients, helping to effectively adapt the large-scale pretrained model to various tasks and meanwhile aiming to maintain its original generalization ability.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
-
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning 13 Sep 2021 · 3 repositories · arXiv:2109.05687Syntology ran 0 of 3 samples · 3 unverified
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Language Modeling | 1 |
| Language Modelling | 1 |
| Large Language Model | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections