Aioli: A Unified Optimization Framework for Language Model Data Mixing

TL;DR

Aioli introduces a new online optimization method for dynamically adjusting data mixture proportions in language model training, outperforming existing approaches and a simple stratified sampling baseline across multiple datasets.

Contribution

The paper unifies existing data mixing methods into a common framework and proposes Aioli, a novel online method that accurately estimates mixing laws to improve language model training.

Findings

Aioli outperforms stratified sampling on all tested datasets.

Existing methods often set mixing law parameters inaccurately.

Aioli improves performance in resource-constrained training scenarios.

Abstract

Language model performance depends on identifying the optimal mixture of data groups to train on (e.g., law, code, math). Prior work has proposed a diverse set of methods to efficiently learn mixture proportions, ranging from fitting regression models over training runs to dynamically updating proportions throughout training. Surprisingly, we find that no existing method consistently outperforms a simple stratified sampling baseline in terms of average test perplexity. To understand this inconsistency, we unify existing methods into a standard framework, showing they are equivalent to solving a common optimization problem: minimize average loss subject to a method-specific mixing law -- an implicit assumption on the relationship between loss and mixture proportions. This framework suggests that measuring the fidelity of a method's mixing law can offer insights into its performance. Empirically, we find that existing methods set their mixing law parameters inaccurately, resulting in the inconsistent mixing performance we observe. Using this insight, we derive a new online method named Aioli, which directly estimates the mixing law parameters throughout training and uses them to dynamically adjust proportions. Aioli outperforms stratified sampling on 6 out of 6 datasets by an average of 0.27 test perplexity points, whereas existing methods fail to consistently beat stratified sampling, doing up to 6.9 points worse. Moreover, in a practical setting where proportions are learned on shorter runs due to computational constraints, Aioli can dynamically adjust these proportions over the full training run, consistently improving performance over existing methods by up to 12.012 test perplexity points.