arXiv:2606.00888v1 Announce Type: new Abstract: Dynamic Sparse Training (DST) offers a promising paradigm for improving the training and inference efficiency of deep neural networks; however, we find that in large language model training, DST can suffer from optimization instability, manifested as loss spikes after topology updates. In this work, we show that the naive use of standard Adam-based optimizers leads to a cold-start issue for newly regrown parameters, resulting in excessively large updates and disrupted training dynamics. To address this issue, we propose Sparse Memory-Efficient Tr
The increasing scale and computational demands of large language models necessitate innovation in training efficiency to overcome resource constraints and improve accessibility.
Improving memory efficiency and training stability for LLMs directly impacts the viability and cost of developing advanced AI, potentially lowering barriers to entry and accelerating progress.
New methodologies for dynamic sparse training will make it more practical to scale LLMs with reduced memory and computational footprints, addressing current bottlenecks.
- · AI researchers and developers
- · Cloud computing providers
- · Semicondutor manufacturers (GPU)
- · Companies without efficient training methods
More powerful and complex LLM architectures can be trained with existing or reduced hardware resources.
Accelerated development cycles for AI models lead to faster commercialization of advanced AI applications.
Reduced compute costs might democratize access to cutting-edge AI model development, fostering wider innovation.
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