Memory-Efficient Techniques for LLM Training and Fine-Tuning

Recent advancements in the field of large language model (LLM) training and fine-tuning have focused on developing memory-efficient techniques to address the growing computational and memory demands. These innovations aim to optimize the training process without compromising model performance. Key approaches include leveraging low-rank approximations and adaptive gradient methods, which allow for full-parameter learning while significantly reducing memory overhead. These methods not only enhance the efficiency of the training process but also demonstrate improvements in model performance across various benchmarks. The integration of second-order optimization techniques and adaptive rank reduction strategies has shown promise in accelerating convergence and achieving better results within limited iteration budgets.

Noteworthy Developments

• Natural GaLore: Introduces a memory-efficient optimizer that incorporates second-order information, significantly improving perplexity and fine-tuning accuracy while using less memory.
• AdaRankGrad: Proposes an adaptive gradient-rank method that reduces memory requirements during training by dynamically adjusting the rank of gradients, leading to enhanced model performance.
• NoRA: Demonstrates the critical role of initialization in low-rank matrix factorization, significantly improving convergence rates and performance in fine-tuning tasks.