  Instruction-tuning language models has become a crucial step in aligning them
for general use. Typically, this process involves extensive training on large
datasets, incurring high training costs. In this paper, we introduce a novel
training data selection based on the learning percentage of the samples. We
assert that current language models possess the capability to autonomously
select high-quality training data, leading to comparable or improved
performance compared to training on the entire dataset. Our experiments span
different-sized models, revealing that this characteristic holds for models
ranging from 1B (small) to 13B (large) in size. Moreover, we demonstrate an
interesting finding that the data hardness transfers across model sizes, and a
smaller 350M model can effectively curate high-quality training data with hard
samples for a larger 13B model, resulting in an equally or superior
instruction-tuned model compared to training on the complete dataset. Utilizing
open-sourced OPT and Llama-2 models up to 13B in size, two publicly available
instruction-tuning training datasets and evaluated by both automatic metrics &
humans, our paper introduces a novel approach to training data selection,
showcasing a more efficient alternative.
