DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
Authors: name: Sang Michael Xie, name: Hieu Pham, name: Xuanyi Dong, name: Nan Du, name: Hanxiao Liu, name: Yifeng Lu, name: Percy Liang, name: Quoc V. Le, name: Tengyu Ma, name: Adams Wei Yu

The mixture proportions of pretraining data domains (e.g., Wikipedia, books,
web text) greatly affect language model (LM) performance. In this paper, we
propose Domain Reweighting with Minimax Optimization (DoReMi), which first
trains a small proxy model using group distributionally robust optimization
(Group DRO) over domains to produce domain weights (mixture proportions)
without knowledge of downstream tasks. We then resample a dataset with these
domain weights and train a larger, full-sized model. In our experiments, we use
DoReMi on a 280M-parameter proxy model to find domain weights for training an
8B-parameter model (30x larger) more efficiently. On The Pile, DoReMi improves
perplexity across all domains, even when it downweights a domain. DoReMi
improves average few-shot downstream accuracy by 6.5% over a baseline model
trained using The Pile's default domain weights and reaches the baseline
accuracy with 2.6x fewer training steps. On the GLaM dataset, DoReMi, which has
no knowledge of downstream tasks, even matches the performance of using domain
weights tuned on downstream tasks.