Title: AC-ODM: Actor–Critic Online Data Mixing for Sample-Efficient LLM Pretraining
Primary area: foundation or frontier models, including LLMs
Keywords: Large Language Models, Online Data Mixing, Pretraining data mixing, Reinforcement learning

Abstract: Pretraining data coverage and composition strongly influence the generalization of large language models (LLMs). While recent data-mixing approaches transfer domain weights learned by a small proxy model to a larger one to reduce computational costs and carbon footprint, they are typically static and ignore training dynamics. Online Data Mixing (ODM) mitigates this with a multi-armed bandit sampler but overlooks intra-domain interactions. We introduce AC-ODM, an actor–critic online data-mixing method that treats the LLM as the environment, uses auxiliary actor–critic networks to dynamically adjust domain sampling weights, and encodes intra-domain interactions through the reward. AC-ODM supports (i) a non-proxy mode that co-trains the actor–critic with the target LLM from scratch, and (ii) a proxy mode that first trains the actor–critic with a small, trainable proxy LLM and then transfers the learned actor to guide the target LLM’s pretraining. Empirically, the proxy mode incurs additional wall-clock time relative to the non-proxy mode but delivers stronger target-LLM performance. Across both modes, AC-ODM enables efficient, adaptive data mixing and accelerates target-model convergence, with negligible per-step wall-clock overhead. On Pythia-1B pretraining over The Pile and SlimPajama, AC-ODM-410M (a policy learned with a 410M-parameter proxy) reaches the optimal validation perplexity of ODM using 71\% and 65\% fewer training steps, respectively. It achieves a 27.5\% relative improvement in zero-shot MMLU accuracy, a 2.23$\times\$ higher pass@1 on HumanEval, and an average +3.44\% accuracy gain across five additional benchmarks. We further show that AC-ODM maintains the fastest pretraining convergence on LLaMA3-style architectures compared to prior data-mixing baselines.

TL;DR: AC-ODM dynamically mixes data with an actor–critic policy, speeding LLM pretraining (up to 71% fewer steps) and improving accuracy (+27.5% MMLU).