No Need to Talk: Asynchronous Mixture of Language Models

ICLR 2025 Conference Submission12727 Authors

Keywords: language models, distributed learning, divide and conquer, efficient inference

Abstract:

We introduce SMALLTALK LM, an innovative method for training a mixture of language models in an almost asynchronous manner. Each
model of the mixture specializes in distinct parts of the data distribution, without the need of high-bandwidth communication between the nodes training each model. At inference, a lightweight router directs a given sequence to a single expert, according to a short prefix. This inference scheme naturally uses a fraction of the parameters from the overall mixture model. Our experiments on language modeling demonstrate that SMALLTALK LM achieves significantly lower perplexity than dense model baselines for the same total training FLOPs and an almost identical inference cost. Finally, in our downstream evaluations we outperform the dense baseline on 75% of the tasks.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 12727