arXiv:2505.23725v3 Announce Type: replace Abstract: DiLoCo is a powerful framework for training large language models (LLMs), enabling larger optimal batch sizes and increased accelerator utilization under networking constraints. However, DiLoCo's performance has been shown to degrade as the number of workers (K) increases (Charles et al., 2025). In this work, we posit that a related but often overlooked factor in DiLoCo's behavior is the choice of inner optimizer, which shapes the pseudogradient used by the outer optimizer. Given the recent success of Muon relative to AdamW for data parallel
The continuous drive for more efficient and scalable training of large language models (LLMs) is pushing research into advanced optimization techniques to address existing bottlenecks.
Improved inner optimizers like Muon can significantly enhance the scalability and performance of LLM training frameworks like DiLoCo, accelerating the development of more capable AI models.
The efficiency of distributed LLM training, particularly at higher worker counts, could improve, leading to faster iteration cycles and potentially larger models being trained economically.
- · AI researchers
- · Hyperscalers
- · Cloud AI providers
- · Large language model developers
- · Less efficient distributed training frameworks
- · Organizations with limited compute resources
Increased efficiency in training large language models at scale.
Faster development and deployment of more sophisticated AI applications and services.
Potential for new AI capabilities to emerge sooner due to reduced training time and cost barriers.
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Read at arXiv cs.LG