Actor-Critic based Online Data Mixing For Language Model Pre-Training

TL;DR

This paper introduces an actor-critic based online data mixing method for language model pretraining that dynamically adapts data domain weights, leading to faster convergence and improved performance on benchmarks.

Contribution

It develops an AC-ODM method that captures domain interactions and adapts sampling strategies using auxiliary actor-critic networks, enhancing pretraining efficiency.

Findings

Achieves 71% faster convergence compared to previous methods.

Improves zero-shot MMLU accuracy by 27.5%.

Outperforms on HumanEval pass@1 by 2.23x.

Abstract

The coverage and composition of pretraining data significantly impacts the generalization ability of Large Language Models (LLMs). To reduce the carbon footprint and financial costs of training, some data mixing methods, which applied the optimized domain weights of a small proxy model to train a larger one, were proposed. However, these methods did not evolute with the training dynamics. The existing online data mixing (ODM) method addressed this limitation by applying the multi-armed bandit algorithm as data sampling strategy. Yet, it did not consider the intra-domain interactions. In this paper, we develop an actor-critic based online data mixing (AC-ODM) method, which captures the varying domain weights by auxiliary actor-critic networks and consider the intra-domain interactions with the reward function. While constructing the dataset to pretrain a large target LLM, we directly apply the actor, which is trained with a small proxy LLM as the environment, as the sampling strategy. The transfer of sampling strategy can not only ensure the efficiency of dynamical data mixing, but also expedite the convergence of pretraining the target LLM. Numerical results demonstrate that AC-ODM-410M, which invokes the sampling strategy obtained by a proxy LLM with 410M parameters, reaching the optimal validation perplexity of ODM 71% faster, and improves performance on the zero-shot MMLU benchmark by 27.5% of accuracy, about 2.23x better on pass@1 of HumanEval benchmark.