datasets essential for the eﬀective training of such models. In addressing these chal-
lenges, the method known as CHILD-TUNING [94] employs data from the target
task to identify a subset of parameters—referred to as the ”child network”—that are
most pertinent to the task, while preserving the pre-trained values for parameters in
the remaining architecture. In a related vein, a list of methods [95–97] introduces a
dynamic parameter selection pipeline speciﬁcally tailored for the eﬃcient ﬁne-tuning of
LLMs. These works adaptively designate a judicious sub-network for staged updates,
leveraging gradient information from back-propagation, thereby achieving notable per-
formance enhancements on domain-speciﬁc tasks, particularly in resource-constrained
environments.
Adapter-based ﬁne-tuning. Diﬀerent from the previous method, in this approach,
additional lightweight layers (adapters) are inserted between existing layers of the pre-
Beyond Efficiency