Training for the Model You Return: Improving Optimization for Iterate-Averaged Language Models
arXiv:2606.25086v1 Announce Type: new Abstract: Many modern Language Model (LM) pipelines return an averaged model, such as an exponential moving average of the training iterates, rather than the final iterate itself. This raises a fundamental question: given that we will return an iterate average, how should we change training to improve the performance of this average? We study this question by formulating optimizer design for the iterate-average estimator as an optimal-control problem. In a continuous-time stochastic quadratic model, we solve for the control strategy that minimizes the erro
This research emerges as AI models, particularly large language models, are becoming increasingly complex and expensive to train, making optimization efficiencies critical.
Improved optimization techniques for iterate-averaged language models can lead to more efficient training, better model performance, and potentially lower compute costs for advanced AI systems.
The way advanced language models are iteratively trained and refined could become more performant and resource-efficient, impacting the development and deployment of future AI applications.
- · AI research institutions
- · Hyperscalers
- · AI software developers
- · Cloud computing providers
- · Inefficient AI training methodologies
- · Companies with suboptimal ML infrastructure
More robust and performant large language models become available for wider applications.
Reduced computational overhead for training state-of-the-art AI models could flatten the cost curve for AI development.
Increased accessibility to advanced AI model training could accelerate innovation across various AI-dependent sectors.
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Read at arXiv cs.LG