arXiv:2506.16659v3 Announce Type: replace Abstract: Training large language models (LLMs) relies on adaptive optimizers such as Adam, which introduce extra operations and require significantly more memory to maintain first- and second-order moments than SGD. While recent works such as GaLore, Fira and APOLLO have proposed state-compressed memory-efficient variants, a fundamental question remains: What are the minimum modifications to plain SGD needed to match state-of-the-art pretraining performance? We systematically investigate this question using a bottom-up approach, and identify two simpl
The continuous growth in LLM size and computational demands necessitates more efficient training methods to sustain progress and broaden accessibility.
Reducing memory requirements for LLM pretraining can significantly lower the cost and increase the speed of developing advanced AI models, impacting the entire AI ecosystem.
Optimized minimalist algorithms could make high-performance LLM training more accessible to a wider range of institutions beyond those with hyperscale resources.
- · AI researchers
- · Smaller AI development companies
- · Cloud infrastructure providers (lower training costs)
- · Hardware manufacturers (broader market for accelerators)
- · Companies heavily invested in current, less efficient optimization tech
- · Firms reliant on memory-intensive training approaches
Reduced memory footprint for LLM training enables larger models or more efficient use of existing compute.
Lower barriers to entry for advanced AI model development could accelerate innovation and diversify the AI landscape.
Increased competition among foundation model developers, potentially democratizing access to powerful AI capabilities.
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Read at arXiv cs.LG