arXiv:2505.23878v2 Announce Type: replace-cross Abstract: Optimizing pretraining data composition is pivotal for LLM generalization. While dynamic mixing outperforms static strategies by capturing evolving training dynamics, current methods fail to reconcile computational efficiency with sample efficiency and structural flexibility for diverse pipelines.We introduce Actor--Critic Online Data Mixing (AC-ODM), which approaches data mixing from a reinforcement learning perspective with a parameterized policy that we theoretically prove to act as a dynamic linear surrogate maximizing the construct
This research addresses the critical need for more efficient and robust LLM pretraining methods as the scale and complexity of these models continue to grow, pushing computational boundaries.
Improving the sample efficiency of LLM pretraining can significantly reduce the computational resources required, making advanced AI development more accessible and cost-effective for a wider range of players.
The introduction of Actor-Critic Online Data Mixing (AC-ODM) potentially accelerates LLM development cycles and lowers the barrier to entry for training large models, impacting the competitive landscape.
- · AI researchers
- · LLM developers
- · Cloud computing providers (reduced egress costs)
- · Smaller AI start-ups
- · Companies with inefficient LLM training pipelines
- · AI compute infrastructure providers (if efficiency drastically reduces demand)
More efficient LLM pretraining leads to faster iteration and deployment of new models.
Reduced training costs could enable a diversification of LLM architectures and applications.
Increased accessibility to advanced LLM training might accelerate the development of AI agents and specialized AI solutions.
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Read at arXiv cs.AI