Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling

📰 ArXiv cs.AI

Learn to train large language models efficiently with dynamic sparsity, overcoming optimization instability and scaling issues

advanced Published 2 Jun 2026

Action Steps

Apply dynamic sparse training to large language models to reduce memory usage
Use modified Adam-based optimizers to address cold-start issues for newly regrown parameters
Implement topology updates to dynamically adjust sparsity patterns during training
Test the stability of the training process using metrics such as loss spikes and optimization trajectories
Configure hyperparameters to balance sparsity and training efficiency

Who Needs to Know This

ML researchers and engineers working on large language models can benefit from this technique to improve training efficiency and scalability

Key Insight

💡 Dynamic sparse training can improve training efficiency, but requires careful optimization to address instability issues

Key Takeaways

Learn to train large language models efficiently with dynamic sparsity, overcoming optimization instability and scaling issues

Full Article

Title: Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling

Abstract:
arXiv:2606.00888v1 Announce Type: cross 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

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